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Job Description – QA & Data Validation Engineer

Experience: 5–6 Years

Location: Pan India

Employment Type: Full-Time

Work Mode: Pan India / Remote or Hybrid as applicable

About the Role

We are looking for an experienced QA & Data Validation Engineer with 5–6 years of hands-on experience in data quality assurance, solution analysis, data validation, SQL, Python, PySpark, Azure Data Factory, Azure Synapse Analytics, and Power BI validation.

The ideal candidate will be responsible for validating large-scale data pipelines, performing source-to-target reconciliation, analyzing business rules, investigating data defects, and ensuring the accuracy, completeness, and consistency of data across source, staging, intermediate, and target systems.

The role requires strong analytical and problem-solving skills along with the ability to work closely with development, data engineering, business, and other stakeholders in an Agile delivery environment.

You will also contribute to the design, development, and maintenance of automated validation frameworks and utilities using Python, SQL, PySpark, Azure Data Factory, and Azure Synapse.

---

Key Responsibilities

1. QA & Solution Analysis

- Analyze business and technical requirements to understand data processing and validation needs.

- Participate in requirement analysis sessions and clarify functional and technical requirements with stakeholders.

- Review solution designs, data flows, mapping documents, interface specifications, and business rules.

- Validate that implemented solutions meet defined business and technical requirements.

- Identify gaps, inconsistencies, ambiguities, and potential data quality issues during requirement and solution analysis.

- Translate business requirements into detailed test scenarios, test cases, and validation conditions.

- Perform end-to-end validation of data processing workflows.

- Ensure data is accurately processed from source systems through intermediate layers to final outputs.

- Validate business rules and transformation logic implemented within data pipelines.

2. Test Planning & Execution

- Prepare comprehensive test strategies, test plans, test scenarios, and test cases for data-intensive applications.

- Execute functional, integration, regression, system, and data validation testing.

- Perform positive and negative testing for different data processing scenarios.

- Validate data pipelines across multiple environments, including staging, testing, and production.

- Identify test data requirements and prepare appropriate datasets for validation.

- Execute SQL queries to validate data processing and transformation results.

- Document test results, observations, defects, and validation evidence.

- Track testing progress and communicate status, risks, issues, and dependencies to stakeholders.

3. Data Validation & Reconciliation

- Perform detailed source-to-target data validation and reconciliation.

- Validate source, intermediate, staging, and output datasets.

- Perform record count validation between source and target systems.

- Verify data completeness, consistency, accuracy, and integrity.

- Validate data transformations against defined business rules.

- Perform field-level and record-level comparisons.

- Validate data types, formats, precision, scale, and null handling.

- Verify schema structure, layout, column names, and column sequence.

- Validate mandatory and optional fields.

- Identify missing, duplicate, truncated, or incorrectly transformed records.

- Analyze invalid records, rejected records, and exception datasets.

- Verify exception and reject-handling mechanisms.

- Compare production and staging data to identify discrepancies.

- Perform reconciliation between files, databases, and reporting layers.

- Validate data across different processing stages and identify the root cause of discrepancies.

4. File & Data Processing Validation

- Validate large-scale datasets across multiple file formats.

- Perform validation of:

 - CSV files

 - Delimited files

 - Fixed-width files

 - Excel files

 - Database tables

 - Structured and semi-structured datasets

- Validate file layouts, headers, delimiters, record formats, and column sequences.

- Verify file-level and record-level counts.

- Analyze source, intermediate, and final output files.

- Validate file-to-database and database-to-file reconciliation.

- Identify incomplete, corrupted, malformed, or invalid records.

- Verify data movement and transformation between different storage locations.

- Validate Azure-to-AWS file transfer processes.

- Ensure transferred files are complete and match the expected source datasets.

---

5. Defect Investigation & Root Cause Analysis

- Investigate data discrepancies and application/data pipeline defects.

- Perform detailed root cause analysis for data quality and validation failures.

- Analyze source data, transformation logic, pipeline execution, database records, and output datasets to identify defects.

- Collaborate with developers and data engineers to resolve identified issues.

- Reproduce defects and provide detailed technical evidence.

- Perform defect impact analysis.

- Conduct retesting and regression testing after defect resolution.

- Monitor recurring data quality issues and recommend preventive solutions.

- Maintain detailed defect documentation and validation results.

---

6. Python Development & Automation

- Develop Python scripts and utilities for data validation and reconciliation.

- Design, develop, and maintain reusable data validation frameworks.

- Automate repetitive data comparison and validation activities.

- Build automated utilities for:

 - Record count validation

 - Data completeness checks

 - Schema validation

 - Column sequence validation

 - Source-to-target comparison

 - Duplicate detection

 - Exception identification

 - Data quality checks

 - Automated reporting

- Develop Python-based validation and reporting utilities.

- Optimize Python scripts for processing large datasets.

- Maintain and enhance existing automation frameworks.

- Implement reusable validation components to improve testing efficiency and coverage.

---

7. SQL Development & Data Analysis

- Write complex SQL queries for data analysis and validation.

- Perform data extraction and comparison using SQL Server / SSMS.

- Validate source and target database records.

- Perform joins, aggregations, subqueries, CTEs, and analytical queries as required.

- Develop SQL queries to identify data mismatches, duplicates, missing records, and transformation issues.

- Validate database tables, schemas, columns, constraints, and relationships.

- Perform record count and reconciliation checks using SQL.

- Analyze SQL Server metrics databases.

- Validate data processing results against expected business rules.

- Troubleshoot data discrepancies using SQL queries.

---

8. PySpark & Large-Scale Data Processing

- Develop and execute PySpark notebooks for large-scale dataset processing and validation.

- Analyze large volumes of structured and semi-structured data.

- Perform data transformation and validation using PySpark.

- Compare large source and target datasets efficiently.

- Implement data quality and reconciliation checks using PySpark.

- Analyze exception, reject, and invalid datasets.

- Optimize data validation processes for large datasets.

- Work with Azure Synapse notebooks and data processing environments.

---

9. Azure Data Factory & Pipeline Testing

- Design and execute validation scenarios for Azure Data Factory (ADF) pipelines.

- Validate pipeline execution, data movement, transformations, and dependencies.

- Monitor pipeline runs and investigate failures.

- Validate source-to-target data movement through ADF.

- Develop and maintain test pipelines using Azure Data Factory.

- Verify pipeline parameters, triggers, activities, and execution results.

- Validate file ingestion and processing workflows.

- Perform end-to-end testing of data pipelines.

- Investigate pipeline-related data discrepancies and failures.

---

10. Azure Synapse Analytics

- Work with Azure Synapse Analytics for data validation and analysis.

- Develop and execute Synapse notebooks using PySpark.

- Validate datasets processed through Synapse pipelines and notebooks.

- Perform data quality and reconciliation checks within Synapse environments.

- Analyze large-scale datasets and processing results.

- Validate data movement between Azure storage, Synapse, databases, and reporting systems.

---

11. Azure Storage & Cosmos DB

- Validate data stored in Azure Storage Accounts and Containers.

- Verify file ingestion, processing, and output data.

- Perform file-level and content-level validation within Azure storage.

- Validate data processing workflows involving Azure Storage.

- Perform data validation in Azure Cosmos DB.

- Verify records, fields, formats, and data completeness within Cosmos DB.

- Investigate discrepancies between source files, Azure storage, databases, and Cosmos DB.

---

12. AWS S3 & Azure-to-AWS Validation

- Validate files stored in AWS S3.

- Perform source-to-target validation for files transferred between Azure and AWS.

- Verify file counts, file names, sizes, formats, and record counts.

- Compare source files with transferred S3 files.

- Validate data integrity after cloud-to-cloud file transfers.

- Investigate missing, incomplete, duplicate, or corrupted files.

- Support end-to-end validation of Azure-to-AWS data movement processes.

---

13. Metrics, Reporting & Power BI Validation

- Extract and validate source system metrics.

- Validate metrics stored in SQL Server databases.

- Perform reconciliation between source metrics, database metrics, and reporting outputs.

- Validate Power BI dashboards and reports against underlying source data.

- Verify report calculations, KPIs, measures, filters, and aggregations.

- Perform file-to-database-to-Power BI reconciliation.

- Validate data displayed in Power BI against SQL Server and source datasets.

- Identify discrepancies between backend data and dashboard results.

- Support reporting and analytics teams with data validation and troubleshooting.

---

14. Production Support & Job Monitoring

- Monitor scheduled data processing jobs and pipelines.

- Perform production validation and health checks.

- Analyze production failures and data discrepancies.

- Support incident investigation and resolution.

- Compare production and staging environments to identify differences.

- Validate production data after deployments and pipeline executions.

- Monitor ECG jobs and provide support for job execution and data processing issues.

- Perform post-production validation and reconciliation.

- Communicate critical production issues and risks to relevant stakeholders.

---

15. Agile Delivery & Stakeholder Collaboration

- Work effectively within an Agile/Scrum delivery environment.

- Participate in sprint planning, daily stand-ups, backlog refinement, sprint reviews, and retrospectives.

- Collaborate with Business Analysts, Developers, Data Engineers, DevOps teams, Product Owners, and other stakeholders.

- Provide timely updates on testing progress and issues.

- Participate in requirement clarification and solution discussions.

- Support release planning and production deployment activities.

- Track work items and defects using Rally.

- Ensure testing activities are aligned with sprint and release timelines.

---

Required Technical Skills

Mandatory Skills

- 5–6 years of experience in QA / Data Validation / Data Testing / Data Quality Engineering.

- Strong experience in SQL and data analysis.

- Hands-on experience with Python development and automation.

- Experience with PySpark and large-scale data processing.

- Strong experience with Azure Data Factory (ADF).

- Experience with Azure Synapse Analytics / Synapse Pipelines / Notebooks.

- Strong understanding of source-to-target data validation and reconciliation.

- Experience in data completeness, record count, schema, layout, and column validation.

- Experience in defect investigation and root cause analysis.

- Experience validating large datasets and multiple file formats.

- Experience with SQL Server / SSMS.

- Experience with Power BI dashboard/report validation.

- Strong understanding of data pipelines and ETL/ELT processes.

Cloud & Data Platform Experience

- Azure Data Factory

- Azure Synapse Analytics

- Azure Synapse Pipelines

- Azure Synapse Notebooks

- Azure Storage Accounts

- Azure Storage Containers

- Azure Cosmos DB

- Azure Privileged Identity Management (PIM)

- AWS S3

- Azure-to-AWS file transfer validation

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Preferred Skills

- Experience developing automated data validation frameworks.

- Experience building automated reporting and reconciliation utilities.

- Knowledge of ETL/ELT testing methodologies.

- Experience working with very large datasets.

- Experience in production data validation and support.

- Knowledge of cloud-based data platforms.

- Experience with Power BI data reconciliation.

- Experience working in Agile environments.

- Experience with Rally or similar Agile project management tools.

- Familiarity with Microsoft Copilot and AI-assisted productivity/automation tools.

---

Key Responsibilities at a Glance

The successful candidate will be responsible for:

- Requirement analysis and clarification

- Business rule validation

- Test planning and execution

- Data quality and data validation

- Source-to-target reconciliation

- Record count and completeness validation

- Schema and layout validation

- Column sequence validation

- Exception and reject data analysis

- Production vs. staging comparison

- SQL-based data analysis

- Python automation

- PySpark development

- Azure Data Factory pipeline testing

- Azure Synapse validation

- Azure Storage validation

- Cosmos DB validation

- AWS S3 validation

- Azure-to-AWS file transfer validation

- Power BI dashboard validation

- SQL Server metrics validation

- Automated reporting

- Defect investigation and root cause analysis

- Production job monitoring and support

- Agile delivery and stakeholder collaboration

---

Candidate Profile

We are looking for a detail-oriented, analytical, and technically strong QA/Data Validation professional who can work independently on complex data validation assignments.

The candidate should be comfortable working with large datasets, writing SQL queries, developing Python automation, analyzing PySpark datasets, validating cloud-based data pipelines, and troubleshooting data discrepancies across multiple systems.

Strong communication and stakeholder management skills are essential, as the role requires regular collaboration with technical and business teams.

---

Education

Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, Data Science, or a related field is preferred.

Experience

5–6 years of relevant professional experience in QA, Data Testing, Data Validation, ETL Testing, Data Quality, Data Engineering QA, or a similar role.

Location

Pan India

Employment Type

Full-Time

Keywords

QA Engineer, Data QA, Data Validation, Data Testing, ETL Testing, Data Quality, SQL, Python, PySpark, Azure Data Factory, ADF, Azure Synapse, Synapse Analytics, Synapse Pipelines, Azure Storage, Cosmos DB, AWS S3, Power BI, SQL Server, SSMS, Data Reconciliation, Source-to-Target Validation, Data Pipeline Testing, ETL QA, Automation Testing, Data Analytics, Root Cause Analysis, Agile, Rally, Cloud Data Testing, Data Engineering QA.

Hiring for Data Scientist / Senior Data Scientist

Exp : 4 - 12 yrs

Edu : BE/B.tech/MCA

Work Location : Pune

Notice Period : Immediate - 15 days

Skills :

4+ years of experience in data engineering, data science, or related domains.

Hands-on experience with SQL, Python, and distributed data systems.

Knowledge of machine learning techniques and statistical analysis.

Experience with cloud data platforms (Azure Data Factory, AWS Glue, GCP BigQuery).

Familiarity with DevOps practices and CI/CD for data pipelines.

Platforms & Operations Experience (Preferred)

- Experience working with Azure, AWS, or Google Cloud data tools.

Operational experience with data orchestration tools (Airflow, ADF, Glue).

Understanding of Kubernetes, Docker, or containerized environments.

Hands-on experience with data warehousing platforms (Snowflake, Redshift, BigQuery).

Experience in monitoring, logging, and alerting operations for data workflows.

SRE / Success Engineering role focused on production operations, reliability, AWS infrastructure, monitoring, incident management, and platform support for the ZT platform.

Core responsibilities include:

• Production monitoring and debugging of live systems.
• Incident investigation, troubleshooting, and problem resolution.
• AWS cloud infrastructure support and maintenance.
• Deployment and operational support activities.
• Supporting a 24x7 production environment.
• Working with GitHub-based development workflows.
• Technical debt remediation and platform improvements.
• Customer issue investigation and support.
• Security and compliance-related work, including FedRAMP initiatives.

Preferred Skills:

AWS (especially S3 and EC2)

Strong debugging and troubleshooting skills

Site Reliability Engineering (SRE) experience

GitHub experience

Basic software development skills

TypeScript/JavaScript knowledge

C# preferred

AI experience is a plus.

Candidate should be a hands-on engineer with strong AWS, SRE, operational ownership, production support, and debugging capabilities, rather than a pure application or full-stack developer.

EMBEDDED AI ENGINEERING POD

AI Implementation Engineer Role

Level: AI Implementation Engineer Senior / Advanced - 6+ years

Practice: Wissen GenAI

Locations: Mumbai / Bengaluru / New York - hybrid, embedded with delivery teams

Reports to: EMBEDDED AI PRACTICE Senior AI Engineering Specialist (Architect); Wissen GenAI Program Lead

Embedded inside enterprise delivery teams, you work closely with global, cross-regional teams to turn prioritized GenAI use cases into production software - building, integrating, and hardening Azure-based AI solutions and accelerating adoption within the teams you join.

You deliver production software and help the teams you join work faster.

As an embedded AI Implementation Engineer, you help convert prioritized use cases into shipped, governed, measurable software.

Key responsibilities

1. Build and ship.

Implement GenAI features end to end on Azure - RAG pipelines, agents, APIs, and UI integrations - against enterprise systems and data.

2. Embed and enable.

Work inside the delivery pods: pair with their engineers, remove blockers, and transfer GenAI skills so adoption sticks after you move on.

3. Productionize.

Add evaluation, observability, guardrails, caching, and CI/CD so prototypes become reliable, cost-efficient services.

4. Integrate securely.

Connect to enterprise data with correct access control, secrets management, and compliance with enterprise security standards and handling of sensitive data.

5. Iterate on quality.

Use evaluation results and user feedback to improve grounding, accuracy, latency, and cost.

6. Measure.

Track delivery and quality metrics that roll up to the program's targets.

Must-have qualifications

• 6+ years in software engineering, with 2+ years building GenAI/LLM applications in production.
• Strong Python (incl. async) and Java (the primary enterprise application stack; Spring a plus); solid API and systems design.
• Azure GenAI hands-on: Azure OpenAI, Azure AI Foundry, Azure AI Search for RAG, Azure AI Document Intelligence (IDP), and Prompt Flow.
• Agent frameworks: Microsoft Agent Framework / Semantic Kernel / AutoGen (or LangChain / LangGraph) and tool / function calling.

Preferred

• RAG fundamentals: embeddings, chunking, vector search, reranking, and grounding.
• Data platforms: Snowflake including Cortex AI (Cortex Search, LLM functions) and SQL, for accessing and grounding on enterprise data.
• Prompt engineering as versioned code; building and running evaluations.
• DevOps: Azure DevOps / GitHub Actions, Docker, AKS / Azure Functions, and observability.
• Financial services or other regulated environments.
• Front-end (React) for AI-assisted UX; streaming and token level operations.
• Azure AI Content Safety and responsible-AI practices.
• Certification: Azure AI Engineer Associate.

What success looks like - first 6 to 12 months

• Multiple GenAI features shipped to production within the embedded delivery pods.
• Measurable adoption and productivity uplift in the teams you support.
• Reusable components adopted from the architects' reference framework.
• Clear contribution to faster time-to-market and lower defect rates.

GEMBA CONCEPTS

Experience: ~3–5 years Type: Full-time

AI/ML Engineer

Location: Bengaluru, India (Hybrid)

About Gemba Concepts

Gemba Concepts is a lean manufacturing and technology consulting firm helping clients across pharma, manufacturing, and logistics

modernize how they operate. We build production systems that sit close to the shop floor — warehouse management, manufacturing

traceability, and an applied AI/ML platform whose flagship use cases are visual quality inspection and predictive maintenance. We’re a

tight engineering team that ships real systems for demanding, often regulated, environments.

The Role

We’re looking for an AI/ML Engineer to take ML capabilities from prototype to production. You’ll own models end-to-end — framing the

problem with stakeholders, building and validating the model, and deploying it as a reliable service that holds up against real-world, messy

industrial data. This is a hands-on building role, not a pure research seat: your work goes into client-facing systems.

What You’ll Do

Build and ship computer vision models for visual quality inspection (defect detection, classification, segmentation) that perform under

real factory lighting, throughput, and edge-case conditions.

Develop predictive maintenance models using sensor/time-series data — anomaly detection, remaining-useful-life estimation, failure

prediction.

Own the full ML lifecycle: data pipelines, feature engineering, training, evaluation, and deployment, with proper versioning and monitoring.

Deploy and serve models in production on Azure (AKS), and keep them healthy — track drift, retraining triggers, and latency.

Integrate LLM-based capabilities (we use the Claude API and self-hosted open models) into delivery and product workflows where they

add leverage.

Collaborate with product, engineering, and domain experts to translate fuzzy operational problems into well-scoped ML solutions — and to

know when ML is not the right answer.

Communicate results and limitations clearly to non-ML stakeholders, including clients.

What We’re Looking For

3–5 years of hands-on experience building and deploying ML models in production (not just notebooks or coursework).

Strong Python and the modern ML stack — PyTorch or TensorFlow, scikit-learn, NumPy/Pandas.

Solid grounding in at least one of: computer vision (CNNs, object detection/segmentation, image preprocessing) or time-series /

anomaly detection.

Practical MLOps experience: containerization (Docker), model serving, experiment tracking, and deploying on a cloud platform — Azure /

Kubernetes (AKS) is a strong plus.

Comfort working with imperfect, real-world data — labeling strategy, class imbalance, data drift, and validation that reflects production

reality.

Good engineering hygiene (Git, testing, code review) and the ability to write code others can build on.

Nice to Have

Experience with industrial / manufacturing data or regulated environments (pharma, 21 CFR Part 11 awareness).

Hands-on LLM integration experience — RAG, prompt engineering, working with APIs or self-hosted models (vLLM, Qwen, etc.).

Edge deployment experience (running CV models on-device / near the line).

Exposure to data pipeline tooling and orchestration.

What You’ll Get

Real ownership of ML systems that go into production for serious clients.

A lean, senior-heavy team where you ship fast and learn across the stack.

Direct exposure to applied AI in manufacturing — a domain where the work has tangible, physical impact

GEMBA CONCEPTS

Experience: ~3–5 years Type: Full-time

AI/ML Engineer

Location: Bengaluru, India (Hybrid)

About Gemba Concepts

Gemba Concepts is a lean manufacturing and technology consulting firm helping clients across pharma, manufacturing, and logistics

modernize how they operate. We build production systems that sit close to the shop floor — warehouse management, manufacturing

traceability, and an applied AI/ML platform whose flagship use cases are visual quality inspection and predictive maintenance. We’re a

tight engineering team that ships real systems for demanding, often regulated, environments.

The Role

We’re looking for an AI/ML Engineer to take ML capabilities from prototype to production. You’ll own models end-to-end — framing the

problem with stakeholders, building and validating the model, and deploying it as a reliable service that holds up against real-world, messy

industrial data. This is a hands-on building role, not a pure research seat: your work goes into client-facing systems.

What You’ll Do

Build and ship computer vision models for visual quality inspection (defect detection, classification, segmentation) that perform under

real factory lighting, throughput, and edge-case conditions.

Develop predictive maintenance models using sensor/time-series data — anomaly detection, remaining-useful-life estimation, failure

prediction.

Own the full ML lifecycle: data pipelines, feature engineering, training, evaluation, and deployment, with proper versioning and monitoring.

Deploy and serve models in production on Azure (AKS), and keep them healthy — track drift, retraining triggers, and latency.

Integrate LLM-based capabilities (we use the Claude API and self-hosted open models) into delivery and product workflows where they

add leverage.

Collaborate with product, engineering, and domain experts to translate fuzzy operational problems into well-scoped ML solutions — and to

know when ML is not the right answer.

Communicate results and limitations clearly to non-ML stakeholders, including clients.

What We’re Looking For

3–5 years of hands-on experience building and deploying ML models in production (not just notebooks or coursework).

Strong Python and the modern ML stack — PyTorch or TensorFlow, scikit-learn, NumPy/Pandas.

Solid grounding in at least one of: computer vision (CNNs, object detection/segmentation, image preprocessing) or time-series /

anomaly detection.

Practical MLOps experience: containerization (Docker), model serving, experiment tracking, and deploying on a cloud platform — Azure /

Kubernetes (AKS) is a strong plus.

Comfort working with imperfect, real-world data — labeling strategy, class imbalance, data drift, and validation that reflects production

reality.

Good engineering hygiene (Git, testing, code review) and the ability to write code others can build on.

Nice to Have

Experience with industrial / manufacturing data or regulated environments (pharma, 21 CFR Part 11 awareness).

Hands-on LLM integration experience — RAG, prompt engineering, working with APIs or self-hosted models (vLLM, Qwen, etc.).

Edge deployment experience (running CV models on-device / near the line).

Exposure to data pipeline tooling and orchestration.

What You’ll Get

Real ownership of ML systems that go into production for serious clients.

A lean, senior-heavy team where you ship fast and learn across the stack.

• Direct exposure to applied AI in manufacturing — a domain where the work has tangible, physical impact

Role Overview

We are looking for a Junior AI System Engineer who is eager to build a career in AI systems, automation, backend workflows, cloud infrastructure, and intelligent product operations.

This role offers an opportunity to work closely with experienced engineers, product teams, and AI specialists on real AI-powered systems that support learners, educators, schools, and internal business operations.

At GoSuper EdTech, our cloud infrastructure is built on Google Cloud Platform — GCP. You will get hands-on exposure to GCP-based systems, backend services, AI integrations, deployment workflows, monitoring, cloud storage, databases, and automation pipelines.

You will help design, integrate, test, monitor, and maintain AI-enabled systems using modern tools such as AI APIs, LLMs, automation workflows, backend services, databases, GCP services, cloud deployment tools, and monitoring systems.

This role is ideal if you are curious about AI, comfortable with technical problem-solving, and interested in building reliable systems that connect software, data, cloud infrastructure, automation, and intelligent workflows.

What You’ll Do

• Support the development and maintenance of AI-powered systems, tools, and workflows.
• Assist in integrating AI APIs, LLM platforms, automation tools, and backend services into GoSuper products.
• Work with OpenAI, Gemini, Claude, or similar AI platforms under the guidance of senior engineers.
• Support AI and backend workflows deployed on Google Cloud Platform — GCP.
• Assist with GCP-based services such as Cloud Run, Compute Engine, Cloud Functions, Cloud Storage, Firebase, Firestore, Cloud SQL, BigQuery, Pub/Sub, Cloud Logging, and Cloud Monitoring, based on project needs.
• Help build AI workflows for content generation, chatbot systems, smart recommendations, internal automation, and productivity tools.
• Support backend integrations using Node.js, Python, REST APIs, webhooks, and third-party services.
• Assist in designing and maintaining system workflows that connect databases, applications, AI models, cloud services, and business tools.
• Work with databases such as PostgreSQL, MongoDB, Firebase, Firestore, Supabase, or similar platforms.
• Help test AI outputs, validate workflows, debug issues, and improve system reliability.
• Monitor system performance, API usage, errors, logs, workflow failures, and cloud service health.
• Support deployment, configuration, and maintenance of AI-enabled product features on GCP.
• Collaborate with product managers, developers, designers, QA teams, and business teams to understand requirements and deliver working solutions.
• Participate in daily standups, sprint planning, technical discussions, and team meetings.
• Document AI workflows, system logic, API integrations, prompts, GCP configurations, deployment steps, and troubleshooting processes.
• Continuously learn and apply best practices in AI systems, backend engineering, automation, GCP cloud infrastructure, and production support.

What We’re Looking For

• 6 months to 1 year of experience in AI systems, backend development, software engineering, automation, DevOps support, cloud support, system integration, or relevant internship/project experience.
• Basic understanding of AI tools, LLMs, APIs, automation workflows, and software systems.
• Working knowledge of JavaScript, TypeScript, or Python.
• Basic backend development experience with Node.js, Express, NestJS, FastAPI, or similar frameworks.
• Basic understanding of Google Cloud Platform — GCP or willingness to learn GCP-based deployment and monitoring workflows.
• Understanding of REST APIs, webhooks, third-party integrations, and data flow between systems.
• Interest in AI APIs, prompt workflows, chatbot systems, automation tools, and intelligent product features.
• Basic understanding of databases such as PostgreSQL, MongoDB, Firebase, Firestore, Supabase, or Redis.
• Ability to debug technical issues across APIs, workflows, logs, backend services, and cloud deployments.
• Good analytical thinking and problem-solving ability.
• Ability to write clear documentation for workflows, integrations, cloud configurations, and technical processes.
• Eagerness to learn new tools, AI platforms, system design concepts, GCP services, and cloud technologies.
• Good communication skills to work with technical and non-technical teams.
• Ownership mindset and willingness to take responsibility for assigned tasks.
• Comfortable working in a fast-paced startup environment.

Nice to Have

• Familiarity with AI APIs such as OpenAI, Gemini, Claude, or similar platforms.
• Basic understanding of prompt engineering and LLM-based workflows.
• Exposure to LangChain, LlamaIndex, embeddings, vector databases, or retrieval-augmented generation.
• Basic experience with GCP services such as Cloud Run, Cloud Functions, Firebase, Firestore, Cloud Storage, Cloud SQL, BigQuery, Pub/Sub, Cloud Logging, or Cloud Monitoring.
• Exposure to Google AI tools, Vertex AI, Gemini API, or AI-related services on GCP.
• Experience with automation tools, workflow builders, webhooks, or integration platforms.
• Exposure to Docker, CI/CD pipelines, GitHub Actions, deployment workflows, or cloud-based release processes.
• Experience working with logs, monitoring tools, API testing tools, or debugging platforms.
• Familiarity with Postman, Git, GitHub, Notion, Zoho, Slack, or similar productivity tools.
• Experience building chatbots, AI assistants, internal tools, or automated workflows.
• Personal, academic, internship, or open-source projects related to AI, automation, backend systems, GCP, or cloud tools.
• Interest in SaaS, EdTech, AI-powered products, and startup environments.

What You’ll Gain

• Hands-on experience building AI-powered systems in a real startup environment.
• Practical exposure to AI APIs, LLM workflows, automation systems, backend services, and GCP cloud infrastructure.
• Mentorship from senior engineers and product leaders.
• Experience working across AI, backend engineering, databases, APIs, integrations, deployment, system monitoring, and cloud operations.
• Opportunity to contribute to real product features used by learners, educators, schools, and institutions.
• Exposure to SaaS product development, EdTech workflows, AI-driven business solutions, and GCP-based production systems.
• Learning culture that encourages experimentation, feedback, and continuous improvement.
• Opportunity to understand how AI systems are designed, deployed, monitored, scaled, and improved in production.
• Access to Cult Elite and Cult Play Pass, offering wellness and lifestyle benefits to keep you energized and inspired.

Compensation

• Competitive salary with performance-based bonuses.
• Equity ownership through ESOPs — own a piece of the company you help build.
• Flexible remote work options with occasional Bengaluru office meetups.
• Health and wellness perks, including Cult Elite membership and Cult Play Pass for employees.
• Learning and development support to help you grow in AI systems, backend engineering, automation, SaaS, and GCP cloud technologies.
• Team retreats, virtual hangouts, and a collaborative work culture.

We are looking for a AI System Engineer who is eager to build a career in AI systems, automation, backend workflows, cloud infrastructure, and intelligent product operations. Apply in https://gosuperedtech.com/career/ai-system-engineer

About the role

We are building AI systems that read, understand and act on real business documents, bank statements, financial reports, policy documents and forms and putting them into production where accuracy and cost both matters.

This is not a research role and it is not a prompt-writing role. You will own features end to end: pick and deploy open-source models, build the pipelines around them, measure whether they actually work on our documents, drive the cost per document down, and keep the whole thing running in production.

You will work closely with the engineering and product teams, and your work will be directly used by business users from day one.

What you will do

Deploy and evaluate open-source models

• Select, deploy and benchmark open-source LLMs and vision-language models for specific, narrow use cases not general chat.
• Build evaluation sets from real documents and define what "good" means numerically (field-level accuracy, extraction recall, hallucination rate) before shipping.
• Run structured comparisons between models and approaches, and write up the trade-offs so the team can make a decision.
• Apply quantization, batching and other optimizations to fit models into a sensible GPU budget.

Build and optimize AI orchestration

• Design multi-step pipelines that combine deterministic code, ML models and LLM calls and know when not to use an LLM.
• Optimize for latency, cost and reliability: caching, batching, request routing, fallback tiers, retries and graceful degradation.
• Instrument pipelines so failures are visible and traceable rather than silent.

Ship to production

• Package models and services with Docker, expose them behind clean APIs, and deploy them to our GPU and CPU infrastructure.
• Handle the unglamorous production concerns: cold starts, timeouts, concurrency limits, versioning, rollback and monitoring.
• Own on-call-style responsibility for the AI features you build, including cost tracking.

Must-have skills

Programming & engineering

• Strong Python: type hints, async/await, dataclasses/Pydantic, clean module design, testing.
• REST API development with FastAPI (or Flask/Django with a willingness to move to FastAPI).
• Git, code review discipline, and the ability to write code someone else can maintain.
• Comfortable in Linux and on the command line.

Machine learning fundamentals

• Working knowledge of PyTorch and the Hugging Face ecosystem (transformers, tokenizers, accelerate).
• Understanding of inference-time concepts: tokenization, context windows, batching, precision (FP16/BF16/INT8), memory footprint.
• Ability to read a model card and a paper well enough to judge whether a model fits a use case.

Document processing

• Hands-on experience with at least two of: pypdfium2, PyMuPDF, pdfplumber, pdfminer.six, Docling, Unstructured, Surya, DocTR, LayoutLM family.
• Practical OCR experience (Tesseract, PaddleOCR, or a cloud OCR) and an understanding of when OCR is the wrong tool.
• Experience extracting tables from PDFs and dealing with merged cells, multi-line rows, and inconsistent column layouts.

Strongly preferred

You will be a much stronger candidate with any of these. We do not expect all of them.

Model serving & optimization

• vLLM, TGI, Ollama, llama.cpp, or Triton Inference Server.
• Quantization formats and tooling: GGUF, AWQ, GPTQ, bitsandbytes, ONNX Runtime, INT8 export.
• Serverless GPU platforms: Modal, RunPod, Replicate, Baseten including cold-start and container-lifecycle management.
• LoRA / QLoRA fine-tuning with PEFT for narrow, task-specific improvements.

Vision-language models

• Practical use of open VLMs: Qwen2.5-VL, InternVL, Granite Vision, Molmo, Phi-Vision, or similar.
• Awareness of where VLMs hallucinate especially on numeric and financial content and patterns for constraining them (using the model for layout only, sourcing values from the text layer, constrained decoding).

Orchestration & pipelines

• Workflow orchestration: Dagster, Airflow, Prefect, or Temporal.
• Async job patterns: Celery, RQ, or platform-native spawn/poll patterns.
• LLM orchestration frameworks (LangGraph, LlamaIndex, Haystack) with the judgement to know when plain Python is a better answer.
• Structured output enforcement: Instructor, Outlines, XGrammar, JSON schema / tool-use modes.

Evaluation & observability

• Building golden datasets and regression suites for extraction tasks.
• Eval tooling: promptfoo, DeepEval, Ragas, or in-house harnesses.
• LLM tracing and monitoring: Langfuse, Arize Phoenix, LangSmith, OpenTelemetry.

Nice extras

• Rule engines and policy evaluation (Open Policy Agent / Rego, Drools, rule-engine).
• Experience in fintech, lending, insurance or accounting documents.
• Handling of PII and data-security practices in document pipelines.
• Contributions to open-source ML or document-processing projects.

Why join us

• Real production ownership from month one your work goes to actual users, not a demo.
• Genuinely hard technical problems in document AI, not wrappers over an API.
• Small team, short decision cycles, direct access to leadership.
• Budget and freedom to evaluate and adopt new open-source models as they land.

To apply: send your CV along with a short note on one AI system you have taken to production what it did, what the accuracy was, and what broke.

Are you looking to work in the cutting edge area of applying data-science to help global customers get a better insight into their health? If so, read on and apply.

Role Name: Senior Data Scientist

Science Team | Full-Time | In-Office | Bangalore

The Role

The Ultrahuman Science Team builds the algorithms behind the Ring, M1 CGM, blood and urine biomarkers, and Performance Lab assessments. We are hiring a Senior Data Scientist to own those algorithms end to end: from the raw sensor signal to a model that is shipped, monitored, and trusted in users' hands.

This is a build role with real scope. In a typical month you will improve a production algorithm, root-cause a metric users are complaining about, and stand up the data pipeline the next model needs. The common thread is ownership: you take a vague question and return a working answer, without waiting to be handed scope.

What You'll Do

·      Own algorithms end to end: sleep staging, activity detection, sensor-derived metrics, and health scores. You frame the problem, build the features, train and evaluate the model, and see it live

·      Ship models, not notebooks: you prove a change on our own cohort before it reaches users, and a model is done only when it runs in production and you can tell how it is behaving

·      Validate against reference standards: design evaluations against gold standards, reference devices, and study ground truth, and know when a result is real and when it is an artifact

·      Own the data layer: cohort extraction, feature pipelines, study data, and raw sensor data, so the next model starts from clean inputs

What This Looks Like in Practice

1. Improving production algorithms - Take an existing production model like sleep staging, root-cause the failure modes against reference data, and ship a fix you can defend with numbers.

2. Building new models - Train an activity classifier on raw sensor data, design the labeled data collection that expands it, and pick the operating point so false positives never erode trust.

3. Proving it before it ships - Run a new steps algorithm against reference-device cohorts, decide with data when it is ready, and monitor how it behaves after rollout.

Who You Are

The two things we can't coach

·      High ownership, end to end: you take a problem from a vague question to a shipped model without waiting to be handed scope, and you can point to something you owned from raw data all the way to production

·      Hungry for more scope: you have outgrown your current role and want problems biggerthan your title, with the technical depth to be trusted with them

Also important

·      You've worked with human health data: wearables, physiological signals, or clinical data.

If your experience is close but not exact, show us why you will ramp fast

·      You've built at a startup: or somewhere small enough that nobody handed you clean data, clear specs, or a mature ML platform

·      You work like it's 2026: coding agents and AI tooling are part of how you build every day, and you can tell which new capabilities are worth adopting

·      You communicate: you can explain a model and its limits to a product manager, an engineer, or a founder, and hold your own with our scientists Core Technical Skills

·      Languages and data: Python and SQL daily, comfortable working in a real codebase

·      Machine learning: PyTorch or TensorFlow, scikit-learn, and gradient boosting, with the judgment to know which the problem needs

·      Advanced machine learning: time series and sequence models, deep learning on continuous physiological signals, and ensembles

·      Statistics and evaluation: hypothesis testing, experiment and A/B design, model evaluation, and error analysis against a reference standard

·      Scale and cloud: Spark or equivalent on large datasets, and AWS, GCP, or Azure

·      Production ML and MLOps: training pipelines, model versioning, deployment, monitoring, and drift detection

·      LLMs and agentic systems: fine-tuning and serving models, building agentic pipelines, and using coding agents to move faster

Experience:

- 4 to 5 years building and shipping machine learning systems. We index on what you have shipped and on trajectory, not the exact number of years; if you are a little earlier but have clearly outgrown your current scope, we want to hear from you.

- Bachelor's or higher in engineering, computer science, statistics, or a related field.

How We Work and Who Thrives Here

- The Science team is small and moves fast, and much of the work has no precedent to copy.

- People do their best work here when they are energized by ambiguity, low on ego, quick to adopt a better idea no matter where it comes from, and comfortable owning something before anyone has told them how. If you need a mature data org, clean labelled datasets, and clear guardrails to thrive, this particular role will not be the right fit, and that is worth knowing up front.

What You'll Gain

·      Ownership of algorithms that hundreds of thousands of people see every morning

·      A dataset most scientists never get to touch: 100M+ nights of sleep and continuous physiological signals at scale

·      Direct collaboration with the engineering, product, and design teams building Ultrahuman

About the role

We are building an operational decision product for a multi-site industrial network — recycling yards, reconditioning shops, inventory, freight, and scrap pricing.

The people who run that network today value inbound material, choose where to cut it, move recovered components to shops, and decide whether to sell or hold scrap. Those calls are made in spreadsheets, on different clocks, with incomplete numbers. We are replacing the spreadsheet with ranked options in dollars, and a clear reason for each ranking.

Source data is already in Snowflake. This role owns the gold layer

everything else depends on: valuation, forecasts, and a finance-facing measure of whether the recommendations paid off. If the grain is wrong, the recommendation is wrong.

What you’ll do

• Design and ship the Snowflake gold layer for the decisions above: on-hand inventory by grade and site, receipts and shipments, realised vs index prices, facility master, freight by lane, shop cost, demand and monthly commitments, and a log of recommendations accepted or overridden.
• Turn raw ERP and shop-floor extracts into tables with an explicit grain (lot, load, site × grade × month — not “whatever the source table was”).
• Build production incremental models in Snowflake (dbt or equivalent) across dev, test, and prod, with tests on uniqueness, grain, and freshness.
• Reconcile data that does not share a clean key across systems — inventory on one side, shop outcomes on the other — and document what can and cannot be joined.
• Surface missing or weak inputs (freight, capacity, operating cost) as labelled gaps. Do not drop a dimension to make a pipeline look complete.
• Work with yard ops, shop ops, and finance so a number in the product can be traced back to a source row and survive a challenge.
• Publish data contracts for downstream forecasting and for an auditable value ledger (recommendation vs what would have happened anyway).

What we’re looking for

• 5+ years building production analytical tables used by operators or finance, not only BI dashboards.
• Expert SQL: window functions, snapshot vs transaction grain, slowly changing inventory, incremental models.
• Snowflake in production — schemas, warehouses, roles, tasks, cost-aware design.
• Dimensional or medallion modeling you have shipped for inventory, cost, or multi-site operations.
• Python for profiling, tests, and supporting SQL models.
• Evidence you have been wrong about a column, found it in real data, and corrected the model.
• Comfort in the room with operators and finance, not only with other engineers.

Nice to have

• dbt on Snowflake, with tests in the merge.
• Manufacturing, remanufacturing, recycling, or metals.
• ERP / MES data (Infor LN or similar) already landed in a warehouse.
• Commodity price or freight/lane-rate data.
• Feature tables or point-in-time snapshots for machine learning.
• Numbers that had to pass a finance or audit review.
• Gold inventory and movement spine: grade × site × time, tested.
• Incremental models from at least two source databases into gold, with freshness alerting.
• Source-to-gold map for the tables the valuation calc needs — real, assumed, and still missing.
• A written position on the hardest cross-system join: what links, at what grain, and what does not.

For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day.

Our India Global Capability Center isn't just supporting global operations—we’re leading global innovation. After scaling rapidly into a best-in-class hub, we deliver the product innovation and enterprise capabilities that accelerate our global growth, profitability, and scale. As we expand Smartsheet India, we’re searching for Senior AI/ML Ops Engineers who crave variety and ownership. You’ll have the opportunity to work across multiple teams and disciplines, building a versatile skillset while solving the complex challenges of a global platform.

You Will:

• Designing, Developing and overseeing the strategy and architecture of scalable and reliable AI/ML Ops platforms / pipelines
• Model Deployment: Package and deploy AI/ML services to production, ensuring they are reproducible and interpretable
• CI/CD Pipeline Development: Design and implement automated CI/CD (Continuous Integration/Continuous Deployment) pipelines to accelerate model deployment using tools
• Infrastructure Management: Provision and optimize infrastructure for training and serving, utilizing Docker, Kubernetes, or serverless platforms
• Monitoring & Observability : Implement post-deployment monitoring for model performance, data drift, and latency using tools. Experience in Monte Carlo is preferable
• Automation: Automate retraining and data pipeline workflows to ensure models stay accurate over time.
• Manage the deployment of foundation models, fine-tuning workflows, and Retrieval-Augmented Generation (RAG) stacks (Vector DBs, Knowledge Graph. Experience with AWS Bedrock is preferable
• Resource Optimization: Manage GPU/CPU utilization to minimize cloud costs while maintaining low-latency inference for users
• Collaboration: Work closely with data scientists, data engineers, and software engineers to bridge the gap between model development and production.
• Version Control & Governance: Manage versioning for data, code, and models using tools like MLflow.
• Security & Compliance: Implementing data security measures, ensuring compliance with data governance policies, and protecting sensitive data
• Technology Evaluation and Innovation: Staying abreast of emerging data technologies and exploring opportunities for innovation to improve the organisation’s data infrastructure
• Troubleshooting and Problem Solving: Diagnosing and resolving complex data-related issues, ensuring the stability and reliability of the data platform
• Perform other duties as assigned

You Have:

• Enterprise SaaS software solutions with high availability and scalability
• Solution handling large scale structured and unstructured data from varied data sources
• Experience in building and maintaining AI/ML Ops platform systems ensuring scalability, reliability, efficiency and security
• Working with Product engineering team to influence designs with data, AI and analytics use cases in mind
• In depth experience in System design, AI/ML Frameworks and tools involving large Petabytes of data with Databricks Lakehouse ecosystem
• AI/MLOps workflows on Databricks , MLFlow, Mosaic AI Agent Framework, Unity Catalog, Vector Search, Knowledge Graph
• Knowledge of AI/ML frameworks like LangChain, LangGraph for AI/ML Ops pipeline integration
• Cloud Platforms: Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP). Experience in AWS hosted data platform is preferable
• Programming languages like Python and SQL
• Modern software engineering practices like Kubernetes, CI/CD, IAC tools (Preferably Terraform), Observability, monitoring and alerting
• Solution Cost Optimisations and design to cost
• Legally eligible to work in India on an ongoing basis

Get to Know Us:

At Smartsheet, your ideas are heard, your potential is supported, and your contributions have real impact. You’ll have the freedom to explore, push boundaries, and grow beyond your role. We welcome diverse perspectives and nontraditional paths—because we know that impact comes from individuals who care deeply and challenge thoughtfully. When you’re doing work that stretches you, excites you, and connects you to something bigger, that’s magic at work. Let’s build what’s next, together.

Equal Opportunity Employer:

Smartsheet is an Equal Opportunity (EEO) employer committed to fostering an inclusive environment with the best employees. It is our policy to provide equal employment opportunities to all qualified applicants in accordance with applicable laws in the US, UK, Australia, Germany, Costa Rica, Japan, Bulgaria, India, and Singapore. All qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veteran or disabled status, or genetic information. 

If there are preparations we can make to help ensure you have a comfortable and positive interview experience, please let us know.

Job application link : https://grnh.se/z7qx2ehx1us

Company: CombineHealth AI

Location: Bangalore, India (Work from Office — 4 days/week)

Experience: 3+ years

Employment Type: Full-time

About Combine Health

Combine Health is building an AI workforce for healthcare revenue cycle management (RCM) — agentic AI systems that automate medical coding, billing, claims, and denial management, and reason through payer policy for healthcare providers in the US. Backed by Y Combinator and Silicon Valley investors, we work with hospitals and health systems to eliminate revenue leakage and reduce manual, error-prone billing work.

We're a small, fast-moving team. Every engineer here owns real product surface area from day one — there's no hiding behind process at a 12-person company.

The Role

We're looking for a Full Stack Engineer to build and ship features across our AI-driven RCM platform — from the interfaces our customers use to review claims and denials, to the backend services that power our AI agents. You'll work closely with founders and a small engineering team, moving fast on a product that directly affects hospital cash flow.

This is not a role where requirements arrive fully specified. You'll be expected to help define what to build, not just how.

What You'll Do

• Design, build, and ship full stack features across the product — from UI to APIs to data models
• Build interfaces for complex, data-heavy workflows (claims review, denial queues, coding dashboards) that need to be fast and legible, not just functional
• Work with backend services that integrate with AI agents, payer systems, and healthcare data sources
• Own features end-to-end: design, implementation, testing, deployment, and iteration based on customer feedback
• Collaborate directly with founders and product on prioritization — this isn't a "tickets handed to you" role
• Write clean, maintainable code and help set engineering practices as the team scales
• Debug and resolve production issues quickly in a live, customer-facing system

What We're Looking For

• 3+ years of professional experience building full stack web applications in production
• Strong hands-on experience with a modern frontend framework (React or similar) and backend framework/language (Python, Node.js or similar)
• Comfort working across the stack: REST/GraphQL APIs, relational databases (Postgres/MySQL), and basic infra/deployment (Docker, cloud platforms like AWS/GCP)
• Ability to take a loosely defined problem and ship a working solution without waiting for a detailed spec
• Solid fundamentals in data modeling, system design, and debugging complex issues in production
• Startup mindset: comfortable with ambiguity, fast iteration, and wearing multiple hats
• Strong communication — you'll be working directly with a small team and need to explain trade-offs clearly

Good to have:

• Experience integrating with AI/LLM APIs or building AI-adjacent product features
• Prior experience at an early-stage startup (seed to Series A)

Why Join Combine Health

• Work on a product with real, measurable impact — faster payments, fewer denials, less manual work for healthcare teams
• Small team, high ownership — your work ships and matters immediately
• Backed by Y Combinator and top investors, early enough that your decisions shape the product
• Direct access to founders and fast decision-making, no layers of bureaucracy

How to apply

• Please click on the link and fill in the details: https://forms.gle/8tX3sXpFZQKotB9e9 

To apply, share your resume along with links to relevant projects or GitHub/portfolio.

Job Description

We are looking for an experienced Data Engineer with strong expertise in Python, ETL, SQL, CI/CD, and DevOps to design, develop, and maintain scalable data pipelines and data processing solutions.

Key Responsibilities

• Design, develop, and maintain scalable ETL/ELT data pipelines.
• Develop data processing solutions using Python.
• Write complex and optimized SQL queries, stored procedures, and data transformations.
• Build and maintain data ingestion and integration workflows.
• Implement data quality, validation, monitoring, and error-handling processes.
• Develop and maintain CI/CD pipelines for data engineering applications.
• Work with DevOps tools and practices for automated build, deployment, and infrastructure management.
• Collaborate with data analysts, data scientists, software engineers, and business teams.
• Optimize data pipelines for performance, reliability, and scalability.
• Troubleshoot production data issues and ensure timely resolution.
• Follow best practices for version control, code quality, testing, and deployment.

Mandatory Skills

• Python
• ETL
• SQL
• CI/CD
• DevOps
• Git / Version Control
• Strong problem-solving and debugging skills

Job Summary

Role Overview

We are looking for an experienced Data Engineer with strong expertise in Python, ETL, Advanced SQL, CI/CD, DevOps, and Data Analytics. The ideal candidate should have hands-on experience designing and developing scalable data pipelines, transforming large datasets, and supporting data-driven applications.

Experience with Google Cloud Platform (GCP) will be an added advantage.

Key Responsibilities

• Design, develop, and maintain scalable ETL/ELT data pipelines using Python and SQL.
• Develop complex and optimized SQL queries, stored procedures, and data transformations.
• Build and maintain reliable data integration workflows across multiple data sources.
• Perform data cleansing, validation, transformation, and quality checks.
• Analyze data and provide insights to support business and technical requirements.
• Implement and maintain CI/CD pipelines for data engineering applications.
• Work with DevOps practices and tools to automate deployments, monitoring, and infrastructure processes.
• Troubleshoot data pipeline failures, performance issues, and production incidents.
• Optimize data processing workflows for performance, scalability, and reliability.
• Collaborate with Data Analysts, Data Scientists, Developers, and other stakeholders.
• Follow best practices for version control, testing, documentation, and deployment.
• Contribute to cloud-based data engineering initiatives, preferably on GCP.

Required Skills

• 5–7 years of hands-on experience in Data Engineering.
• Strong programming skills in Python.
• Strong expertise in Advanced SQL and database concepts.
• Hands-on experience with ETL/ELT processes and data pipelines.
• Good understanding of Data Warehousing and Data Modeling concepts.
• Experience with CI/CD practices and tools.
• Strong understanding of DevOps principles, automation, and deployment processes.
• Strong data analytics and problem-solving skills.
• Experience working with large datasets and performance optimization.
• Good understanding of Git/version control and software development best practices.

Good to Have

• Hands-on experience with Google Cloud Platform (GCP).
• Exposure to GCP data services such as BigQuery, Cloud Storage, Dataflow, Composer, or Pub/Sub.
• Experience with containerization/orchestration technologies such as Docker/Kubernetes.
• Experience with workflow orchestration tools such as Airflow.
• Knowledge of cloud-based data architecture and distributed data processing.

Preferred Candidate Profile

• Strong analytical and problem-solving abilities.
• Good communication and stakeholder management skills.
• Ability to work independently as well as in a collaborative team environment.
• Strong ownership of data pipelines and production systems.
• Candidates who can join at short notice are preferred.

Mandatory Skills

 Data Engineer, Python , ETL, GCP, Advanced SQL, Strong Data Analytics skills, CICD, Devops

About Naicos

Naicos, a fast-paced startup, builds AI-native products for algorithmic commerce: the future of how e-commerce runs. Our first products are already live with paying customers, and we are shipping new ones continuously.

Your Role

You will join the team that creates catalog imagery for sellers at scale, working closely with a Senior Engineer who will train you. You will start on well-defined tasks and grow into writing the prompts and the Python that produce the images.

Who We Are Looking For

• Total experience: 0 to 1 year, internships included

• Python: you can write and debug your own code

• No prior AI or e-commerce experience needed; we will teach you

• Final-year students and recent graduates are welcome to apply

You will be paired with a Senior Engineer and given a structured 90-day ramp. We are hiring for aptitude and attitude, not for a CV.

Skills You Bring

• Python: you can write and debug your own code. This is what we will test, and the only hard requirement.

• Curiosity about AI: you have played with ChatGPT, Claude, Gemini or image generation tools and want to build with them

• Care about detail: you notice when something looks slightly off

Good to have

• Any exposure to image editing, or to Python image libraries such as Pillow or OpenCV; college projects, hackathons or open-source work

What You Will Learn Here

• Prompt engineering for image generation models

• Image manipulation in Python: resizing and interpolation, contrast adjustment, overlaying and joining images

• How a real e-commerce catalog works, and what the marketplaces will and will not accept

Other Relevant Skills

• Communicates clearly in English, written and spoken

• Reliable and organised: you finish what you pick up, and ask for help early

• Willing to do hands-on production work while you learn; the first months mix real output with learning

Educational Qualification

• BE / B.Tech in Computer Science, IT or any engineering discipline

• BCA or MCA

• BSc / MSc in Computer Science, Maths, Statistics or Physics

• Or equivalent practical experience with a portfolio of projects

About Us

Invorto is our Voice AI product, bringing intelligent voice agents to real-world customer and operational use cases. Our voice pipeline is built in Python, running an STT → LLM → TTS architecture on top of the Pipecat framework.

This is a chance to work on hard problems in voice AI — latency, accuracy, naturalness, and reliability — building zero-to-one, owning your area end-to-end, and shipping to production at scale.

Note: This is a customer-facing role, and strong communication skills are essential.

About the Role

We're looking for a Voice AI Research Engineer to join the Invorto team and help build and continuously improve the voice AI systems that power our intelligent voice agents. This role is focused on the specialized craft of voice AI — designing evaluation and automation frameworks that ensure our STT, LLM, and TTS pipeline performs reliably in real-world, production conditions.

What You'll Do

• Design and build automated testing and quality frameworks for our STT → LLM → TTS voice pipeline, built on Pipecat
• Evaluate and benchmark STT, LLM, and TTS/ASR components on accuracy, latency, naturalness, and robustness across accents, languages, and real-world audio conditions
• Work hands-on with STT, TTS, and ASR models — fine-tuning, evaluating, and improving them for production use cases
• Identify failure modes and edge cases across the pipeline (background noise, accents, interruptions, turn-taking, latency, pipeline-stage handoffs) and build systems to catch them before production
• Collaborate closely with engineering to integrate quality checks and automation into the voice agent development lifecycle within the Pipecat-based architecture
• Research and stay current with advances in voice AI, and bring in new techniques, models, and tools to improve pipeline performance
• Work directly with customers to understand real-world voice use cases and translate them into evaluation criteria and quality benchmarks
• Partner with product and engineering to define what "production-grade quality" means for voice agents and drive the team toward it

What We're Looking For

• 4–6 years of experience, with a specialization in voice AI systems and automated quality evaluation
• Hands-on experience with STT (Speech-to-Text), TTS (Text-to-Speech), and ASR (Automatic Speech Recognition) models
• Experience designing and building automated testing/evaluation frameworks for voice or speech systems
• Strong understanding of what drives voice AI quality — accuracy, latency, naturalness, and robustness to real-world variability
• Strong programming skills in Python; familiarity with Pipecat or similar voice pipeline/orchestration frameworks is a plus
• Understanding of STT → LLM → TTS pipeline architectures and the trade-offs involved at each stage
• Research mindset — comfortable exploring new models, techniques, and tools and translating them into practical improvements
• Excellent communication skills — this is a customer-facing role, and you'll regularly engage directly with customers to understand needs and validate quality expectations

About Us

We’re building the next generation of AI-powered business software, and we’re looking for people who want to shape that future with us. With Lumen, we’re reimagining how users interact with CRM — moving beyond screens, menus and dashboards to an intelligent interface where users can simply ask AI to take actions, retrieve knowledge, generate insights and get work done. With Agent Studio, we’re enabling businesses to build, test and deploy their own AI agents for real-world workflows. And with Invorto, we’re bringing AI to voice, allowing businesses to create intelligent voice agents tailored to their customer and operational use cases.

What makes this especially exciting is the stage and scale of the opportunity. You’ll get to work on genuinely hard problems across LLMs, agents, reasoning, orchestration, voice AI, evaluation, reliability and enterprise security — not as isolated experiments, but as products used in real business workflows. You’ll have the opportunity to build zero-to-one, own meaningful parts of the product end-to-end, work closely with customers, experiment rapidly, and see your work reach production at scale.

Why join now? Because the playbook for enterprise AI is still being written. You won’t just be implementing someone else’s roadmap — you’ll help define the product, architecture and experiences that become that playbook. Expect high ownership, fast iteration, hard technical and product problems, direct customer impact, and the chance to build AI systems that have to work reliably in the real world — not just in a demo.

About the Role

We are looking for a QA Engineer who specializes in testing agentic AI platforms. You will design and automate quality processes for systems that involve LLMs, autonomous agents, tool use and orchestration across Lumen and Agent Studio — ensuring that AI-driven workflows behave reliably, safely and predictably in production, not just in a demo.

What You’ll Do

• Design and build automated test suites and evaluation frameworks for agentic AI workflows, including multi-step and tool-calling behaviors.
• Use AI/LLM-based QA tools and evaluation frameworks to test model outputs, agent decisions and end-to-end task completion at scale.
• Define quality metrics and benchmarks for agent reliability, correctness, latency and safety, and track them over releases.
• Identify edge cases, failure modes and regressions specific to non-deterministic AI systems, and build automated checks to catch them early.
• Integrate automated agent/LLM testing into CI/CD pipelines to support fast, reliable iteration.
• Partner closely with AI/ML and backend engineers to reproduce issues, root-cause failures and validate fixes.
• Work with customers and customer-facing teams to understand real-world usage patterns and translate them into test scenarios.

What We’re Looking For

• 2–4 years of QA/test automation experience, including hands-on work testing agentic AI or LLM-based platforms.
• Practical experience using AI-focused QA/evaluation tools to test agent behavior, prompts and model outputs.
• Strong scripting/automation skills (Python preferred) to build and maintain test frameworks.
• Understanding of how LLM-based agents work — tool calling, orchestration, memory, reasoning chains — well enough to design meaningful test cases.
• Comfort working with non-deterministic systems and designing evaluation approaches beyond traditional pass/fail testing.
• Strong communication skills — this is a customer-facing role, and you will be expected to clearly articulate technical concepts, decisions and trade-offs to both technical and non-technical stakeholders, including customers.

Good to Have

• Experience testing voice AI or real-time conversational systems.
• Familiarity with CRM or enterprise SaaS platforms.
• Exposure to enterprise security or compliance testing for AI systems.

Hiring for Python Full Stack Lead

Exp : 6 - 10 yrs

Edu : BE/B.Tech 60 - 65 %

Work Location Pune WFO

Skills : 4+ years of experience in Python

2+ years of experience in React Js

Rest Framework

Lead Exp must

About us

MyRico builds personal AI agents for enterprise, the copilots and digital employees that make humans more productive. The MyRico agents sit at the intersection of enterprise memory, high-end security, and an ever-expanding set of capabilities. We're a small team shipping fast, and the product is live with real customers today.

The role

Full-time · Bangalore

You'll own the systems that make an autonomous agent trustworthy in production. This is not just prompt engineering, and it's not model training - it's that and all the engineering layers in between: model steering, memory architecture, orchestration design, latency optimization, deterministic vs non-deterministic systems tradeoff, deployment, and operator tooling.

Concretely, the kind of work you'd have done here last week would include enhancing agent memory systems, runtime and scheduling reliability and predictability, adding new capability and tools to deployed agents, operator tools, live system debugging and benchmarking various models for price, latency and quality. And that is just last week. We are a small nimble startup rapidly working to address customer needs in this growing space, so things change rapidly.

What we're looking for

• More than 7 years of software engineering, with real production ownership of distributed or stateful systems - you've been paged for something you built and made it not happen again.
• Strong understanding of LLM based native app building, combining classic and model driven applications to get the best of both. You've built on LLMs beyond demos: agent frameworks, tool use, context management, eval fixtures, and you know why "it worked in the transcript" isn't evidence.
• Python and shell in production settings; comfortable in TypeScript/Node. You write boring, testable code and prefer the standard library to a new dependency.
• Systems taste: append-only logs, idempotent reconciliation, fold-the-events state machines, and read-only debugging surfaces feel like home.
• Evidence discipline: tests before features, claims backed by quoted observations, decisions written down.

Nice to have

• Experience running the combination of multi-tenant and single-tenant / on-prem-style fleets with ability to handle per-customer isolation, upgrade paths, migration compatibility in both setups.
• Security instincts for products that touch highly sensitive data and systems, including things like executives' email, calendars, and messages: least privilege, loopback-only services, secrets that never hit a log.
• You already orchestrate AI coding agents in your own workflow and have opinions about where they break.

How we work

Small team, high trust, written decisions. Designs get adversarial review before code; PRs get automated review driven to zero open findings; features aren't done until verified on a live system. AI agents do a large share of the implementation, your leverage is judgment: framing the problem, freezing the right design, and knowing when the machine is wrong.

About the Role

We are looking for an experienced Python Full-Stack Developer with 5+ years of experience to develop and maintain scalable web applications and SaaS products. The role involves working with Python, FastAPI/Flask/Django, ReactJS, REST APIs, databases, AWS, and Docker, while focusing on performance, scalability, and reliable solutions. The ideal candidate should have strong problem-solving skills and the ability to collaborate effectively with cross-functional teams.

Requirements

• 5+ years of hands-on experience in Python Full-Stack Development.
• Strong experience with Python, FastAPI/Flask/Django, ReactJS, JavaScript, HTML5, CSS3
• Good knowledge of REST APIs, Celery, and Redis.
• Experience with MySQL/PostgreSQL and MongoDB.
• Hands-on experience with AWS (EC2, Secrets Manager, ECR) and Docker.
• Proficiency in Git, NumPy, Pandas, and Matplotlib.
• Strong understanding of Data Structures, Algorithms, debugging, and performance optimisation.
• Knowledge of Machine Learning is an added advantage.
• Strong analytical, problem-solving, and communication skills.

Key Responsibilities

• Develop and maintain scalable full-stack applications using Python, FastAPI/Flask/Django, ReactJS, and JavaScript.
• Build and integrate REST APIs, responsive UI components, and third-party services.
• Implement asynchronous processing using Celery and Redis.
• Work with SQL and NoSQL databases including MySQL, PostgreSQL, MongoDB, and Redis.
• Develop data-driven solutions using NumPy, Pandas, and Matplotlib.
• Deploy and manage applications using AWS (EC2, Secrets Manager, ECR) and Docker.
• Use Git for version control and collaborative development.
• Debug, optimize, and ensure application performance, scalability, and reliability.
• Collaborate with cross-functional teams to deliver high-quality solutions and contribute to technical improvements.

Good to Have

• Experience working on SaaS products or large-scale web applications.
• Experience with AWS deployment and CI/CD pipelines.
• Knowledge of cloud architecture and microservices.
• Experience with machine learning or data-intensive applications.
• Experience with frontend state-management solutions such as Redux or Context API.
• Experience mentoring junior developers or leading technical initiatives.

Must of Skills/Experience 

• System Design

• Python

• TensorFlow

• Google ADK or Lang Graph

• Lang Chain , Lang Graph

• Spark

• Agentic AI Design

• ML Ops

• MCP (client and server)

• FastAPI

• Doc Factory

• RAG

• Golang

• LLMs – Gemini, Open AI

• NLP

• Dev Assistant - AI based code - generation

(Qwen or Claude or Copilot)

• CI/CD

• Good in oral and written communication,

collaboration and be a team player

Good to have skills 

• DevOps with K8

• Scripting

• Java

• REST API

• UV

• ReACT

• DocFactory

• Unix

About Naicos

Naicos, a fast-paced startup, builds AI-native products for algorithmic commerce: the future of how e-commerce runs. Our first products are already live with paying customers, and we are shipping new ones continuously.

Your Role

You will drive the research behind our imaging products, finding approaches to hard, unsolved problems in product and apparel imagery that work at production scale. You will run the experiments, prove what is viable, and hand a working approach to the engineering team.

Who We Are Looking For

•     Total experience: 3 years or more, with a strong research orientation

•     Deep learning frameworks in Python: PyTorch or TensorFlow

•     Image processing in Python: OpenCV, Pillow, scikit-image

•     Working knowledge of diffusion and other image generation models

We are looking for a strong research or research-student profile: someone who investigates, experiments and proves an approach, working closely with the AI Architect. Someone who is driven to build solutions, not just desk research.

AI Skills and Experience

•     Computer vision: classical CV alongside deep learning.

•     Segmentation, image-to-image translation, geometry and lighting; 

•     Generative imaging: diffusion models, conditioning and control, fine-tuning and LoRA,

•     Reads academic papers, judges what is reproducible, and turns one into a working prototype in days

Good to have

•     3D and rendering; published research or open-source contributions; model optimisation for inference cost

Research and innovative problem solving

•     Comfortable where there is no known answer, and defines the approach yourself

•     Solves problems inventively rather than reaching for the biggest model; many results come from classical image processing, fitment and geometric transformation

Other Relevant Skills and Experience

•     Designs experiments: baselines, measurable success criteria, honest reporting of negative results

•     Explains findings to a non-research audience and guides engineers to production

•     Git and reproducible experiment tracking (Weights & Biases, MLflow or similar)

Educational Qualification

•     BE / B.Tech / ME / M.Tech in Computer Science

•     BE / B.Tech / ME / M.Tech in any discipline with proven Computer Vision coursework or work

•     MSc / MS in Computer Science, Maths, Statistics or Computer Vision

•     PhD in Computer Vision or Machine Learning: an advantage, not a requirement

•     Reputed Tier 1 university preferred

About Naicos

Naicos, a fast-paced startup, builds AI-native products for algorithmic commerce: the future of how e-commerce runs. Our first products are already live with paying customers, and we are shipping new ones continuously.

Your Role

You will be part of a team, working with a Tech Lead to build catalog products that help sellers list and sell across marketplaces. You will have the opportunity to engineer products from scratch using the latest in AI technology. You will own both the prompting and the code around it, shipping real features from scratch.

Who We Are Looking For

•     Total experience: 2 to 3 years

•     AI / LLM experience: 1 year or more, hands-on

•     Core language: Python (essential)

•     Working knowledge of system integration and APIs

You will start with straightforward, common-sense prompting, prompt optimisations and fine-tuning, and grow into deeper integration work, with a Tech Lead guiding you through broader technical decisions.

AI Skills and Experience

•     Python: strong, hands-on, everyday coding ability. 

•     Practical prompt engineering against LLM APIs (Gemini / OpenAI / Claude): structured output, JSON-schema enforcement, few-shot examples

•     Making a non-deterministic model give reliable, repeatable output: temperature control, validation, retry when the output doesn't match the expected shape

Good to have

•     LangGraph, Langchain, Airflow; Vibe programming (AI-assisted programming)

•     Awareness of token usage and costs; agent harness and agentic workflows; exposure to fine-tuning prompts

Learning ability

•     Ability and willingness to absorb new concepts, new programming languages, and the e-commerce domain

Other Relevant Skills and Experience

•     REST API integration, including marketplace seller APIs (Amazon SP-API, Flipkart Seller API)

•     Data transformation and schema mapping: CSV, JSON, XML; Databases such as MongoDB

•     Git, CI and code review discipline; can turn a spec document into acceptance criteria

Educational Qualification

•     BE / B.Tech in Computer Science, IT or a related engineering discipline

•     MCA

•     BSc / MSc in Computer Science, Maths or Statistics, with demonstrated coding work

•     Or equivalent practical experience with a strong portfolio of shipped work

About Naicos

Naicos, a fast-paced startup, builds AI-native products for algorithmic commerce: the future of how e-commerce runs. Our first products are already live with paying customers, and we are shipping new ones continuously.

Your Role

You will lead a small team building the image generation products that create catalog imagery for sellers at scale. You will set the technical approach, judge what is feasible, and stay hands-on, writing prompts and Python alongside your team.

Who We Are Looking For

•     Total experience: 3 to 5 years

•     AI experience: 1 year or more, hands-on

•     Python: strong, everyday coding ability

•     Experience guiding 2 to 4 engineers, formally or informally

You will own delivery for this team, working with the Architect on the wider technical direction. This is a hands-on lead role, not a management role.

AI Skills and Experience

•     Python: strong, hands-on. You will still write code every day.

•     Prompt engineering for image generation models, or equivalent depth in another AI domain, with the judgement to know when a prompt is likely to fail

•     Image manipulation in Python (Pillow, OpenCV): resizing and interpolation, contrast and tonal adjustment, overlaying and joining images, other basic image manipulation funcitons

•     Evaluates the feasibility of a requirement in the AI and LLM domain, and finds creative solutions to stated problems

Good to have

•     LangChain, LangGraph and Airflow; vibe programming (AI-assisted programming)

•     Agent harness and agentic workflows; image-to-image and inpainting workflows; batch processing at volume; awareness of generation cost per image

Learning ability

•     Absorbs new tools and the e-commerce catalog domain, and brings the team along

Other Relevant Skills and Experience

•     Guides 2 to 4 engineers: assigns work, reviews output, unblocks and grows them

•     Git and code review discipline; owns quality and daily output for the team

•     Communicates clearly with product, catalog and business stakeholders

Educational Qualification

•     BE / B.Tech in Computer Science, IT or a related engineering discipline

•     MCA

•     BSc / MSc in Computer Science, Maths or Statistics, with demonstrated coding work

•     Or equivalent practical experience with a strong portfolio of shipped work

About Naicos

Naicos, a fast-paced startup, builds AI-native products for algorithmic commerce: the future of how e-commerce runs. Our first products are already live with paying customers, and we are shipping new ones continuously.

Your Role

You will be part of a team building the image generation products that create catalog imagery for sellers at scale. You will have the opportunity to engineer products from scratch using the latest in AI technology. You will write the prompts and the Python around them, turning a raw product photo into finished, marketplace-ready images.

Who We Are Looking For

•     Total experience: 1 to 2 years

•     AI experience: 1 year or more, hands-on

•     Python: strong, everyday coding ability

•     Working knowledge of image manipulation in Python (Pillow, OpenCV)

You will work with a Senior Engineer who sets the direction. Your focus is producing reliable image output day to day, and improving how it is generated.

AI Skills and Experience

•     Python: strong, hands-on, everyday coding ability.

•     Writing good prompts for image generation models: precise, repeatable, and tuned to a required output rather than a lucky one-off

•     Image manipulation in Python (Pillow, OpenCV): resizing with the right interpolation, contrast and tonal adjustment, sharpening, overlaying and joining images, and writing out new image files

Good to have

•     LangChain, LangGraph and Airflow; vibe programming (AI-assisted programming)

•     Agent harness and agentic workflows; image-to-image and inpainting workflows; batch processing; awareness of generation cost per image

Learning ability

•     Willingness to absorb new tools and techniques, and the e-commerce catalog domain

Other Relevant Skills and Experience

•     Comfortable with image file formats, resolution and colour basics (JPEG, PNG, WebP, DPI, RGB)

•     Git and code review discipline; can follow a defined process and hold a daily output bar

•     Attention to visual detail: can spot when a generated image is subtly wrong

Educational Qualification

•     BE / B.Tech in Computer Science, IT or a related engineering discipline

•     MCA

•     BSc / MSc in Computer Science, Maths or Statistics, with demonstrated coding work

•     Or equivalent practical experience with a strong portfolio of shipped work

Senior Software Engineer (Full Stack)

Location: Bangalore / Chennai / Pune / Hyderabad

Experience: 7+ Years

Notice Period: Immediate Joiner

Job Summary:

We are looking for a Senior Software Engineer with strong Full Stack development expertise in Streamlit, Python FastAPI, Databricks, and Databricks Apps. The ideal candidate will build data-driven web applications and deploy scalable solutions within the Databricks ecosystem.

Key Responsibilities:

• Design and develop interactive applications using Streamlit.
• Build scalable backend services and APIs using Python FastAPI.
• Develop and optimize data solutions on Databricks.
• Deploy and manage applications using Databricks Apps (Mandatory).
• Integrate front-end applications with backend APIs and data services.
• Collaborate with data engineers, data scientists, and business teams.
• Ensure application performance, reliability, and security.
• Participate in code reviews, testing, and release management activities.

Required Skills:

• 7+ years of software development experience.
• Strong experience with Python programming.
• Hands-on expertise in FastAPI.
• Experience developing applications using Streamlit.
• Strong Databricks development experience.
• Mandatory experience with Databricks Apps deployment and management.
• Experience with REST APIs and microservices architecture.
• Familiarity with Git, CI/CD pipelines, and Agile methodologies.

Preferred Skills:

• Experience with Azure Databricks.
• Knowledge of AI/ML application development.
• Understanding of cloud-native architectures.
• Front End: Streamlit
• Middleware: Python FastAPI
• Backend: Databricks
• Hosting Environment: Databricks Apps (Mandatory)

Hiring for Data Analyst

Exp : 5 - 7 yrs

Edu : BE/B.Tech

Work Location : Noida WFO

Skills :

Expertise in SQL Server, including database design, performance tuning, query optimization, and security.

Hands-on experience developing ETL solutions using SSIS, Azure Data Factory (ADF), and Python.

Job Description:

Experience: 10+ Years

Job Summary

We are looking for an experienced Azure Fabric Data Architect to lead the design and implementation of an enterprise data platform on Microsoft Fabric. The role involves architecting scalable data solutions, defining data governance, and enabling AI-driven analytics for a global financial services client.

Key Responsibilities

• Design end-to-end data architecture using Microsoft Fabric.
• Build enterprise Lakehouse, Data Warehouse, and OneLake solutions.
• Define data ingestion, ETL/ELT, governance, security, and performance strategies.
• Lead architecture for AI-powered analytics, AI Agents, and enterprise chatbots using Azure AI services.
• Work with business stakeholders to translate requirements into technical solutions.
• Mentor engineering teams and provide technical leadership.

Required Skills

• Microsoft Fabric (Data Factory, Lakehouse, Data Warehouse, OneLake)
• Azure Data Engineering
• Power BI
• Azure AI Services / Azure OpenAI
• Data Architecture & Data Modeling
• SQL, Python
• Azure DevOps, CI/CD
• Strong stakeholder management and solution design experience
• 
  

Preferred: Experience in Capital Markets or Financial Services and Microsoft Azure/Fabric certifications.

NOTE: One technical round is mandatory to be taken F2F from office.

Skill Set

Large language,Artificial Intelligence,Machine Learning

- 4–7 years of experience in software engineering/AI roles

- Strong programming skills in Python or TypeScript (Java/Go is a plus)

- Hands-on experience with LLMs, RAG pipelines, and AI frameworks

- Experience building APIs and working with distributed systems

- Familiarity with Kubernetes, Docker, and CI/CD pipelines

- Experience with cloud platforms (AWS/Azure/GCP)

Excellent communication

Role Overview 

As a Data Scientist, you will work with business stakeholders, AI engineers, and domain experts to transform data into actionable insights and intelligent solutions. You will develop machine learning models, perform statistical analysis, and contribute to AI-driven products that create measurable business impact. 

Key Responsibilities 

Data Science & Machine Learning 

• Analyze structured and unstructured data to identify patterns, trends, and business opportunities.  
• Perform exploratory data analysis (EDA), feature engineering, and data preparation.  
• Develop, evaluate, and optimize machine learning models for prediction, classification, clustering, and forecasting.  
• Apply statistical techniques to solve business problems and validate model performance.  
• Design and execute experiments to improve model accuracy and business outcomes.  

 AI Solution Development 

• Collaborate with AI Engineers, Data Engineers, and domain experts to build AI-powered solutions.  
• Translate business requirements into scalable data science approaches.  
• Contribute to Generative AI and advanced analytics initiatives where applicable.  
• Document methodologies, model performance, and key findings.  

 Required Technical Skills 

• Strong programming skills in Python and SQL for data analysis, feature engineering, and machine learning.  
• Strong understanding of Statistics, Probability, Linear Algebra, and Calculus as applied to machine learning and data science.  
• Experience with Exploratory Data Analysis (EDA), data preprocessing, feature engineering, feature selection, and handling missing or imbalanced data.  
• Good understanding of Supervised, Unsupervised, and Ensemble Machine Learning algorithms, including their assumptions, strengths, limitations, and appropriate use cases.  
• Strong knowledge of Regression, Classification, Clustering, Time Series Forecasting, Dimensionality Reduction, Recommendation Systems, and Anomaly Detection techniques.  
• Experience with Model Evaluation, Cross-Validation, Hyperparameter Optimization, Bias-Variance Trade-off, Feature Importance, Explainable AI (XAI), and Performance Metrics.  
• Understanding of Statistical Inference, Hypothesis Testing, Probability Distributions, Sampling Techniques, Confidence Intervals, and A/B Testing.  
• Experience translating business problems into analytical approaches and developing scalable, data-driven solutions.  
• Working knowledge of Generative AI, Large Language Models (LLMs), Prompt Engineering, and Retrieval-Augmented Generation (RAG) is preferred. 

Preferred Qualifications 

• Bachelor's or master's degree in computer science, Artificial Intelligence, Data Science, Statistics, Mathematics, Engineering, or a related field.  
• 2–4 years of experience developing machine learning or data science solutions.  
• Experience working on end-to-end data science projects in a business environment.  

 Nice to Have 

• Exposure to Generative AI, LLMs, RAG, or Agentic AI.  
• Experience with Computer Vision or Natural Language Processing (NLP).  
• Familiarity with cloud-based AI platforms.  
• Knowledge of construction, engineering, manufacturing, or industrial domains.  
• Participation in hackathons, research, Kaggle competitions, or open-source projects.  

 Soft Skills 

Strong analytical and problem-solving skills, effective communication and collaboration, ownership mindset, adaptability, continuous learning, and a passion for innovation.

Bachelor’s degree in Computer Science, Information Technology, or related field

Min 15 yrs total exp

More than 4 years of experience in AI development and implementation

Proficiency in Python programming - min 5 yrs

Hands-on experience with Azure AI and cloud services - 4 yrs

Proven leadership and project management skills

About Us:

Datum builds market intelligence solutions for the retail industry. We transform public and proprietary data into actionable insights that help retail brands decide where to expand, compete, and grow. We are a small, fast-moving team that works closely with customers and

believes in shipping impactful products quickly.

About the Role

We're looking for a Full Stack Platform Engineer to build and own our end-to-end product ecosystem, including:

● Data acquisition through scalable web scraping and ETL pipelines

● Customer-facing analytics platform

● Geospatial analysis engine powering retail insights

You'll work across frontend, backend, data engineering, cloud infrastructure, and geospatial systems while collaborating directly with the founder and customers.

Key Responsibilities

● Build scalable web scrapers and ETL pipelines

● Develop customer-facing features using React/Next.js

● Design REST & WebSocket APIs

● Work with PostgreSQL/PostGIS and geospatial data

● Own deployment, CI/CD, monitoring, and cloud infrastructure

● Translate customer feedback into product features

Must-Have Skills

● 3–6 years of Full Stack development experience

● Python (FastAPI/Django)

● React or Next.js with JavaScript/TypeScript

● Web scraping using Scrapy, Playwright, Selenium, or BeautifulSoup

● PostgreSQL (PostGIS preferred)

● REST APIs, JWT/OAuth

● Docker, Git, AWS/GCP/Azure

● Understanding of proxy rotation and anti-bot techniques

Good to Have

● Mapbox, Leaflet, or Google Maps Platform

● Airflow, Redis, Kafka, or SQS

● Experience with large-scale scraping (Google Maps, Zomato, Justdial, etc.)

● Geospatial analytics or retail domain experience

● Startup experience with end-to-end ownership

Greetings From NAM INFO Pvt

Performance Engineer III

Experience: 6–8 Years

Location: Siruseri – EB6 / Gitanjali Park – SEZ

Job Summary

We are looking for an experienced Performance Engineer to work on large-scale distributed systems and cloud platforms in a high-traffic e-commerce environment. The role involves performance testing, monitoring, troubleshooting, and tuning applications to improve scalability, reliability, and overall system performance.

Key Responsibilities

• Design and execute performance testing strategies including load, stress, and capacity testing.
• Use tools such as JMeter, Locust, or LoadRunner for performance testing.
• Automate performance tests as part of CI/CD pipelines.
• Analyze performance issues using thread dumps, heap dumps, and TCP dumps.
• Monitor applications using APM tools such as New Relic or Dynatrace.
• Use Splunk for log analysis, dashboards, queries, alerts, and root-cause analysis.
• Tune application servers such as Tomcat, Node.js, and Spring Boot.
• Analyze network performance using tools such as Wireshark, ExtraHop, and Riverbed.
• Monitor systems and proactively identify performance bottlenecks.
• Analyze browser performance using Lighthouse, Chrome DevTools, Catchpoint, and WebPageTest.
• Work closely with Developers, SRE, QA, and DevOps teams.
• Participate in code reviews and ensure performance best practices are followed.

Required Skills

• 6–8 years of experience in Performance Engineering / Performance Testing.
• Strong hands-on experience with JMeter is preferred.
• Experience with Splunk for log analysis and troubleshooting.
• Strong knowledge of APM tools such as New Relic or Dynatrace.
• Experience with performance test planning, execution, analysis, reporting, and defect tracking.
• Good understanding of microservices architecture.
• Experience with performance tuning of Tomcat, Node.js, and Spring Boot.
• Strong troubleshooting and root-cause analysis skills.
• Good understanding of CI/CD and performance test automation.

Good to Have

• Programming experience in Java, Python, or Groovy.
• Experience analyzing heap dumps, thread dumps, and TCP dumps.
• Knowledge of Wireshark or CloudShark.
• Experience with Fiddler and Chrome DevTools.
• Knowledge of Azure, AKS, or GCP.
• Certifications in Azure, Splunk, or New Relic.

Responsibilities

·        Build and operate the agentic loop: trigger → orchestration → agent execution → output to JIRA → human accept/reject → next agent, across design, coding, review, and testing agents.

·        Implement model routing and retry logic across a provider-agnostic model layer (e.g., Claude via AWS Bedrock, self-hosted or alternative models as cost/sovereignty hedges), including business-continuity fallback if a given provider becomes unavailable.

·        Own token cost control and context window management — per-agent and per-run budgets, circuit breakers that halt runaway execution, and cost observability tied back to JIRA.

·        Stand up and maintain observability, alerting, and monitoring across the agent fleet (e.g., Langfuse or equivalent), so agent health, cost, and quality are visible in real time.

·        Implement agent governance and safety guardrails: deterministic pre/post hooks gating every LLM call, kill switches, prompt injection prevention and mitigation, and audit logging.

·        Integrate the harness with JIRA as the system of record and other business systems as needed, ensuring every agent action, decision, and human override is tracked with no side channels.

·        Pair directly with client engineers throughout — this is capability transfer, not black-box delivery. You'll document, demo, and hand over as you build.

·        Work in outcome-based delivery stages (spike → architecture sign-off → build → pilot) with gated milestones tied to working software demos, not fixed artifact checklists.

·        Participate actively in team discussion and design decisions — this team expects engineers to challenge ideas constructively and speak up, not defer silently.

Must-Have Experience

·        Hands-on production experience building agentic systems(not tutorial-level or personal-project experience.) Candidates should be able to speak concretely about systems they've shipped.

·        Practical experience with agentic frameworks such as LangChain, LangGraph, or equivalent orchestration frameworks.

·        Experience with LLM orchestration and model routing across multiple providers/models, including fallback and retry design.

·        Working knowledge of agent governance: guardrails, human-in-the-loop approval flows, kill switches, and audit trails.

·        Practical understanding of prompt injection risks and mitigation techniques.

·        Experience with token cost management and context window/memory handling at production scale — this is a named governance requirement for the engagement, not a nice-to-have.

·        Strong Python (or equivalent) engineering background, comfortable working in AWS environments (Bedrock/AgentCore exposure a strong plus).

·        Experience with observability/monitoring tooling for distributed or agentic systems (e.g., Langfuse, Datadog, or equivalent).

·        Comfortable working with JIRA/Atlassian APIs or similar ticketing-system-of-record integrations.

Nice to Have

·        Direct experience with AWS Bedrock AgentCore, Temporal (or similar workflow orchestration), or LiteLLM-style model gateways.

·        Exposure to Cursor or other AI-native IDEs in a production engineering context.

·        Experience with self-hosted open-weight models (e.g., DeepSeek, GLM) as cost or sovereignty hedges alongside commercial APIs.

·        Financial services or other regulated-industry background.

·        Familiarity with Claude Code, Claude Cowork, or Claude Skills.

Qualifications

·        Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.

·        3-5+ years in software/platform engineering, with a meaningful portion of that time specifically on agentic or LLM-orchestration systems (not general ML or data engineering alone).

Relevant Experience

·        Already built this kind of system and can talk through the trade-offs from experience, not theory.

·        Comfortable operating with ambiguity - technology choices (frameworks, specific models, tooling) are expected to evolve during the engagementand milestones are tied to outcomes rather than fixed deliverables.

·        Will contribute opinions - quiet execution without a point of view is not a fit for this team.

Senior Database Administrator (DBA)

Database Reliability & Performance  •  Senior Individual Contributor

• Experience: 6–9 years in production database administration / engineering
• Level: Senior Individual Contributor (hands-on)
• Function: Data Platform — reliability, performance, and governance across relational and distributed SQL & NoSQL DBs.

About the Role

We are hiring a Senior Database Administrator to own the health, performance, and reliability of the data layer behind a high-scale, multi-tenant platform. Our environment spans several independent PostgreSQL database instances, a cache-first read tier, change-data-capture pipeline, analytics and archival stores. Should be capable of implementing NoSQL stores (Cassandra / ScyllaDB) & MySQL  end to end as we scale.

This is a hands-on senior IC role for someone who has run demanding production databases and wants full ownership: partitioning and maintenance, performance tuning, monitoring, uptime, disaster recovery, and the security and data-governance documentation that keeps a regulated, multi-tenant platform audit-ready. You will work closely with, and take direction from, engineering leadership — translating priorities into a well-run, well-documented data platform.

What You'll Own

PostgreSQL Depth

• Administer multiple production PostgreSQL instances (v16+), including schema/DDL review, migrations, and release coordination.
• Design and maintain table partitioning strategies (range/hash/time-based) and automate partition lifecycle — creation, retention windows, and safe drops.
• Own query and instance performance tuning: EXPLAIN/ANALYZE, indexing strategy, autovacuum/bloat management, connection pooling (e.g. PgBouncer), and parameter-group tuning.
• Execute zero-downtime schema changes using expand–migrate–contract / blue-green patterns; assess lock impact and avoid long ACCESS EXCLUSIVE locks on hot tables.
• Manage replica topology (writer/reader endpoints), replication lag, and read-scaling as load grows.
• Partner with app teams on migration tooling (e.g. Flyway) and paired forward/rollback scripts; enforce idempotent, reviewed, tested DDL.

NoSQL & Scale

• Data-model, deploy, and operate distributed NoSQL clusters — Cassandra and/or ScyllaDB — including partition/clustering key design, compaction strategy, repair, and node operations (bootstrap, decommission, scale-out).
• Lead the planned migration of high-volume payload tables from partitioned PostgreSQL to Cassandra/ScyllaDB: capacity planning, dual-write/backfill design, cutover, and validation.
• Tune distributed-store read/write paths, consistency levels, and cluster sizing for predictable latency at scale.
• Support the cache-first architecture (Redis) and change-data-capture / streaming pipelines (e.g. Kafka) that move data to analytics and cold-archival stores.

Operations, Reliability & DR

• Own database uptime and SLOs; build and maintain monitoring, alerting, and dashboards for the key signals (latency, replication lag, CPU/IO, connections, vacuum lag, error rates).
• Define and regularly test backup and disaster-recovery strategy — RPO/RTO targets, point-in-time recovery, restore drills, and cross-AZ / future multi-region readiness.
• Drive incident response for database-related issues: triage, mitigation, root-cause analysis, and follow-up actions.
• Write and maintain runbooks (partition maintenance, failover, archival, DR, migration procedures) and participate in an on-call rotation for the data layer.
• Establish production-change discipline: pre-flight and post-deploy verification, rollback criteria, and backup verification before destructive operations.

Security, Governance & Documentation

• Implement and maintain database access control — least-privilege roles, schema-scoped permissions, and row-level security (RLS) for multi-tenant isolation.
• Own data-governance documentation: PII maps, per-table retention policies, audit-readiness, and the controls behind privacy-regulation compliance (e.g. GDPR / DPDP-style erasure and legal-hold workflows).
• Author and maintain architecture decision records (ADRs), schema documentation, and monitoring/runbook docs so the data platform is legible to the wider team.
• Uphold encryption-at-rest/in-transit posture and safe handling of sensitive columns; keep the data layer aligned with the platform's compliance requirements.

Coding & Automation Bar (Required)

This role is automation-first, not click-ops. The right candidate treats repetitive database work as something to script away and can drop into application code when a problem demands it.

• Strong scripting in Python and Bash for automation of maintenance, backups, partition management, backfills, and health checks.
• Comfortable with infrastructure-as-code (e.g. Terraform) and configuration management (e.g. Ansible) for reproducible database infrastructure.
• Solid SQL and PL/pgSQL; able to read and debug application-layer database code and, worst case, write or patch application code (e.g. Python/Node/Java) to unblock a fix.
• Fluent with Git-based workflows, code review, and CI for database migrations and tooling.

Required Qualifications

• 6–9 years operating relational databases in demanding production environments, with deep PostgreSQL expertise (internals, partitioning, replication, performance tuning).
• Hands-on experience running at least one distributed NoSQL store in production — Cassandra or ScyllaDB strongly preferred (other wide-column/distributed stores considered).
• Proven ownership of backup/DR, monitoring, and incident response for production databases.
• Strong scripting/automation background (Python, Bash) and experience with IaC.
• Experience with managed cloud databases (AWS RDS or equivalent) and understanding of instance/parameter-group tuning, replicas, and failover.
• Experience designing for multi-tenancy and data isolation at scale.
• Clear written communication — able to produce runbooks, ADRs, and governance docs a team can rely on.

Nice to Have

• Experience migrating workloads from PostgreSQL to Cassandra/ScyllaDB (or similar relational-to-distributed migrations).
• Familiarity with Redis as a cache tier and with CDC / streaming pipelines (e.g. Kafka, Debezium).
• Exposure to Flyway (or Liquibase), pgTAP or similar schema testing, and PgBouncer.
• Working knowledge of privacy regulations (GDPR, India DPDP) and audit/compliance processes.

How You Work

• Ownership mindset — takes a directive and runs it to done, with sound judgment on trade-offs.
• Reliability-first and detail-oriented; disciplined about verification, rollback, and documentation.
• Collaborative in a high-performing engineering team; communicates clearly with developers and leadership.
• Pragmatic and scale-aware — designs for where the platform is going, not just where it is.

Location: Pune/Bengaluru

As a Software Engineer, you will tackle a highly challenging role within our Cyber Security Engineering integration development team. You will be responsible for the End-to-End (E2E) delivery of projects from gathering customer requirements to deploying high-performance data exchange solutions on-prem or in the cloud. We are looking for a Systems-Level Thinker who can seamlessly transition between building complex integrations and architecting high-throughput, low-latency backends. Because each integration may require a different technical approach, we value engineers who love reskilling, following agile best practices, and maintaining an evolving mindset.

Core Responsibilities

Customer and Stakeholder Collaboration

● Customer Delight: Maintain a strong customer-first attitude while designing, building, and deploying products.

● Technical Communication: Articulate complex technical trade-offs clearly to both business stakeholders and engineering teams.

● Pipeline Management: Manage customer relationships effectively to ensure a continuous flow of project scope, maintaining clear team backlogs.

Job Requirements

● Experience: 3+ years of proven software engineering experience, with a track record of leading small engineering teams or mentoring junior devs.

● High-Throughput Backend Focus: 3+ years of experience engineering robust, real-time backends specifically using Go (Golang) or Java.

● Polyglot and Integration Capability: Deep expertise in at least one or two additional languages/frameworks from our ecosystem: Python, TypeScript, Node.js, React.js, Angular, C#, or C++.

● Computer Science Fundamentals: Excellent knowledge of complex Data Structures, algorithms, and their correct situational usage.

● Adaptability: A passionate programmer who is genuinely open and excited to learn new software development skills as required by changing project demands.

Bonus / Added Advantages

● AI & Intelligent Automation: Hands-on knowledge of AI concepts, Large Language Models (LLMs), and experience building or integrating AI agents to automate complex workflows or security processes.

● Cybersecurity Domain: Experience in the cybersecurity space (SIEM, SOAR, or platform security).

● Modern Web Tech: Strong experience in developing software using modern web technologies.

● E2E Lifecycle: Proven background handling a project completely from discovery to cloud/on-prem deployment.

● Open Source: Active contributions to the Open Source community. (Please share your GitHub/GitLab links in your application!)

Job Title: AI Engineer Intern

Location: Bangalore(Onsite)

Experience Level: Recent Graduates.

Salary Range: 25K/month

Application Link:https://beyond.ciltriq.com/apply/fit

Description:

Join an early-stage, high-velocity team building AI agents that automate end-to-end compliance investigations for banks.

The product is already live, and you'll work directly on the core platform-building and evaluating agent systems that power real compliance investigations.

This is a hands-on engineering role with real ownership, working on production systems from day one.

6 Months Internship.

Full time offer based on Performance.

Requirements:

- Build and improve agent harness for compliance investigation use cases

- Develop and evaluate LLM-based extraction, classification, and reasoning components

- Contribute to synthetic data generation for agent testing and evaluation

- Write evaluation harnesses to measure agent accuracy and reasoning quality

- Document agent behaviour, evaluation results, and design decisions

- Track latest agentic AI research and bring relevant techniques into production (context management, long-term memory, agent learning)

- Train and fine-tune machine learning models from scratch for compliance-specific use cases

- Strong foundation in Python; comfortable with ML libraries

- Exposure to or strong curiosity about LLMs and agentic frameworks

- Independent – able to own problems, not just sub-tasks, in a small team

- Good written communication; able to document and present your work clearly

- Prior coursework or projects in ML, NLP, or distributed systems is a plus

- No prior fintech or compliance experience required

AI Engineer (Preferred Years of Experience: 8 years)

We are seeking a highly skilled Full Stack AI Engineer to design, build, and scale intelligent applications across the full technology stack. This role combines strong backend and frontend engineering expertise with applied AI/ML implementation in enterprise cloud environments.

You will work closely with product managers, architects, data scientists, and DevOps teams to deliver production-grade AI-powered solutions that are secure, scalable, and aligned with business objectives.

This is a hands-on engineering role requiring experience across application development, AI model integration, cloud architecture, and DevSecOps practices.

Key Responsibilities

AI / Machine Learning

• Design and implement AI/ML solutions for real-world business use cases.
• Integrate ML models (e.g., forecasting, classification, NLP, computer vision) into production-grade applications.
• Develop APIs and services that expose AI capabilities securely and efficiently.
• Optimize model performance, latency, scalability, and monitoring in production.
• Implement model lifecycle management (training, deployment, monitoring, retraining).

Backend Development

• Design and develop scalable RESTful and/or GraphQL APIs.
• Build microservices-based architectures.
• Implement authentication, authorization, and secure API access.
• Develop data pipelines and integrate with structured and unstructured data sources.
• Ensure high availability, performance tuning, and observability.

Frontend Development

• Develop responsive, user-friendly web applications.
• Build interactive dashboards and AI-driven user experiences.
• Integrate frontend applications with backend AI services.
• Ensure accessibility, usability, and performance optimization.

Cloud & DevOps

• Deploy applications and models in cloud environments (Azure, AWS, or GCP).
• Implement CI/CD pipelines for application and model deployment.
• Apply infrastructure-as-code (Terraform, ARM, Bicep, etc.).
• Implement monitoring, logging, and alerting.
• Ensure security compliance and enterprise-grade governance.

Architecture & Collaboration

• Participate in solution architecture and design discussions.
• Translate business requirements into technical solutions.
• Collaborate with cross-functional teams (product, security, data, UX).
• Contribute to technical standards, best practices, and code reviews.

Required Qualifications

• 8 years of full stack software engineering experience.
• Hands-on AI/ML implementation in production environments.
• Strong proficiency in: Python (FastAPI, Flask, or Django), JavaScript/TypeScript (React, Angular, or Vue), REST API development
• Experience with ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow, or equivalent).
• Experience deploying AI workloads in cloud environments (Azure ML, SageMaker, Vertex AI, etc.).
• Experience with relational and NoSQL databases.
• Strong understanding of software engineering principles and design patterns.
• Experience with Docker and container orchestration (Kubernetes preferred).
• Knowledge of secure coding practices and enterprise security standards.

Preferred Qualifications

• Experience with enterprise AI governance and responsible AI frameworks.
• Experience with MLOps and model monitoring tools.
• Knowledge of distributed systems and event-driven architectures.
• Experience with vector databases and semantic search (if applicable to organization).
• Experience in regulated industries (financial services, healthcare, public sector).
• Experience working in Agile/Scrum teams.

Key Competencies

• Strong problem-solving and analytical skills.
• Ability to translate complex AI concepts into scalable technical solutions.
• Excellent communication and stakeholder engagement skills.
• Ownership mindset with the ability to operate independently.
• Strong attention to performance, security, and maintainability.

Strong Python Developer profile with robust AWS exposure

2

Mandatory (Experience 1): Must have 7+ years of hands-on software development experience with at least the recent 4+ years in Python and strong hands on knowledge of AWS

3

Mandatory (Tech skill 1): Must have strong working knowledge of Python.

4

Mandatory (Tech skill 2): Must have good understanding of AWS services including EC2, S3, Lambda, IAM, CloudWatch, and ECS or ECR

5

Mandatory (Tech skill 3): Must be able to write and understand REST APIs

6

Mandatory (Tech skill 4): Experience designing and architecting scalable backend applications/services on AWS, including making decisions around application architecture, APIs, databases, and AWS services

7

Mandatory (Tech skill 5): Must be comfortable with version control tools such as Git, GitHub, Bitbucket, or GitLab

8

Mandatory (Tech skill 6): Must have good understanding of databases such as PostgreSQL, MySQL, or DynamoDB

9

Mandatory (Tech skill 7): Must have familiarity with Linux commands and shell scripting

10

Mandatory (Skill 1): Must have good debugging and problem-solving skills, with the ability to read existing code and make changes independently with light guidance

11

Mandatory (Skill 2): Must have strong communication skills

12

Preferred (Tech skill 2): Experience with AWS CodeCommit, and exposure to AWS CodeBuild, CodeDeploy, CodePipeline, GitHub Actions, Jenkins, or similar CI/CD tools

13

Preferred (Tech skill 3): Basic understanding of CI/CD pipelines

14

Preferred (Tech skill 4): Experience with Docker or container-based applications

15

Preferred (Tech skill 5): Basic knowledge of infrastructure-as-code tools such as Terraform or AWS CloudFormation

Strong Python Developer profile with robust AWS exposure

2

Mandatory (Experience 1): Must have 7+ years of hands-on software development experience with at least the recent 4+ years in Python and strong hands on knowledge of AWS

3

Mandatory (Tech skill 1): Must have strong working knowledge of Python.

4

Mandatory (Tech skill 2): Must have good understanding of AWS services including EC2, S3, Lambda, IAM, CloudWatch, and ECS or ECR

5

Mandatory (Tech skill 3): Must be able to write and understand REST APIs

6

Mandatory (Tech skill 4): Must be comfortable with version control tools such as Git, GitHub, Bitbucket, or GitLab

7

Mandatory (Tech skill 5): Must have good understanding of databases such as PostgreSQL, MySQL, or DynamoDB

8

Mandatory (Tech skill 6): Must have familiarity with Linux commands and shell scripting

9

Mandatory (Skill): Must have good debugging and problem-solving skills, with the ability to read existing code and make changes independently with light guidance

10

Mandatory (Skill 2): Must have strong communication skills

11

Preferred (Tech skill 1): Experience with AWS CodeCommit, and exposure to AWS CodeBuild, CodeDeploy, CodePipeline, GitHub Actions, Jenkins, or similar CI/CD tools

12

Preferred (Tech skill 2): Basic understanding of CI/CD pipelines

13

Preferred (Tech skill 3): Experience with Docker or container-based applications

14

Preferred (Tech skill 4): Basic knowledge of infrastructure-as-code tools such as Terraform or AWS CloudFormation

Job Title : Senior Data Engineer – Databricks

Experience : 14 to 20 Years

Location : HSR Layout, Bangalore

Work Mode : Hybrid – 3 Days WFO

Shift : 11:30 AM – 07:30 PM IST

Positions : 2

Notice Period : Immediate Joiners Only

Interview : 1 Technical Round + 2 Client Rounds

Role Overview :

We are looking for a Senior Data Engineer to build and lead enterprise-scale data platforms for a Switzerland-based commodity client.

The role requires a strong hands-on Data Engineering professional with expertise in Databricks, PySpark, Python, SQL, and AWS, along with technical leadership and stakeholder management experience.

Must-Have Skills :

• 14 to 20 years of Data Engineering experience
• Databricks & Apache Spark / PySpark
• Python & SQL
• AWS Cloud
• Lakehouse Architecture
• ETL / ELT & Distributed Data Processing
• Batch & Streaming Pipelines
• Data Pipeline Optimization & Data Modeling
• CDC & Incremental Processing
• Git, CI/CD & Testing
• Data Quality, Monitoring & Observability
• Technical Leadership & Stakeholder Management

Key Responsibilities :

• Design and build scalable data pipelines using Databricks, PySpark, Python, SQL, and AWS.
• Own data products from design through production.
• Develop batch / streaming pipelines and reusable ETL / ELT frameworks.
• Optimize pipelines for performance, scalability, reliability, and cost.
• Design scalable data architectures and data models.
• Implement data quality, monitoring, lineage, and CI/CD practices.
• Lead technical discussions and mentor engineering teams.
• Collaborate with business stakeholders, architects, product owners, and engineering teams.
• Remain hands-on while providing technical leadership.

Ideal Candidate :

A 14 to 20 years experienced, hands-on Data Engineering leader with strong Databricks + PySpark + AWS expertise, excellent communication, stakeholder management, and experience delivering enterprise-scale data platforms.

🔴 Super Urgent : Only Bangalore-based immediate joiners.

About NonStop io Technologies

NonStop io Technologies is a value-driven company with a strong focus on process-oriented software engineering. We specialize in Product Development and have a decade's worth of experience in building web and mobile applications across various domains. NonStop io Technologies follows core principles that guide its operations and believes in staying invested in a product's vision for the long term. We are a small but proud group of individuals who believe in the 'givers gain' philosophy and strive to provide value in order to seek value. We are committed to and specialize in building cutting-edge technology products and serving as trusted technology partners for startups and enterprises. We pride ourselves on fostering innovation, learning, and community engagement. Join us to work on impactful projects in a collaborative and vibrant environment.

Brief Description

We are looking for a passionate and experienced Backend Engineer (Python) to join our engineering team. The ideal candidate will have strong expertise in backend development, API design, scalable application architecture, and SaaS applications, along with exposure to data processing, ETL workflows, and data integration. Familiarity with React and frontend technologies is an added advantage.

You will collaborate with cross-functional teams to build, enhance, and maintain robust, secure, and high-performance applications while contributing to backend services, data workflows, and cloud-based systems.

Roles and Responsibilities

• Design, develop, and maintain scalable and high-performance web applications and backend services.
• Develop secure, efficient, and reliable backend services and RESTful APIs using Python.(Django/Flask)
• Design and implement scalable microservices and distributed application components.
• Collaborate with frontend engineers and contribute to React-based features or integrations when required.
• Develop, maintain, and support ETL/data processing workflows for extracting, transforming, and loading data across systems.
• Build Python-based data processing scripts, services, and integrations to support application and business requirements.
• Work with relational databases to support data extraction, transformation, validation, and loading processes.
• Collaborate with data and engineering teams to improve data quality, reliability, and processing efficiency.
• Work closely with product managers, designers, QA engineers, DevOps, data engineers, and other stakeholders to deliver high-quality features.
• Write clean, maintainable, reusable, and testable code following software engineering best practices.
• Participate in code reviews and contribute to coding standards, engineering practices, and technical improvements.
• Optimize applications, APIs, databases, ETL processes, and backend services for performance, reliability, and scalability.
• Troubleshoot, diagnose, and resolve application, data processing, and production issues.
• Contribute to system architecture, technical design decisions, and continuous improvement initiatives.
• Develop and maintain automated tests to ensure application quality and reliability.
• Collaborate with DevOps teams to support CI/CD pipelines, containerized deployments, cloud-based applications, and deployment automation.
• Monitor and support backend services and data workflows in development, staging, and production environments

Requirements

• 3 to 6 years of professional experience in Backend Development using Python.
• Strong hands-on experience with Python backend frameworks such as Django and Flask.
• Experience building and supporting SaaS applications, with exposure to microservices, caching, pub-sub, messaging technologies, RESTful APIs, and distributed systems.
• Exposure to ETL processes, data pipelines, data integration, or data processing using Python. (Preferred)
• Understanding of ETL concepts such as data extraction, transformation, validation, loading, and data quality.(Preferred)
• Experience working with relational databases, preferably PostgreSQL, for application development and/or data processing workflows.
• Strong understanding of databases such as PostgreSQL, MySQL, MongoDB, or SQL Server.
• Familiarity with frontend technologies, particularly React, is an advantage.
• Experience with version control systems such as Git.
• Exposure to technologies such as Kafka, Redis, Elasticsearch, Redshift, Nginx, and GraphQL is a plus.
• Exposure to data technologies or tools such as Airflow, Pandas, SQLAlchemy, Spark, or similar data processing frameworks is a plus.
• Strong foundation in computer science, including data structures, algorithms, object-oriented programming, and software design principles.
• Familiarity with CI/CD pipelines and tools such as Jenkins.
• Good understanding of cloud platforms such as AWS, Azure, or GCP.
• Strong understanding of software design principles, scalable application architecture, and data structures.
• Experience with containerization technologies such as Docker.
• Knowledge of automated testing frameworks and best practices.
• Experience working in Agile/Scrum environments.
• Strong problem-solving, analytical, communication, and collaboration skills.

Why Join Us?

• Opportunity to work on a cutting-edge healthcare product
• A collaborative and learning-driven environment
• Exposure to AI and software engineering innovations
• Excellent work ethic and culture

If you're passionate about technology and want to work on impactful projects, we'd love to hear from you!

About the Company

Fluxsolve Technovations Private Limited (incorporated 2023) builds Fuse-OS — an AI-powered Insurance Distribution Operating System. Fuse-OS is a unified enterprise platform that orchestrates people, processes, and insurance ecosystems across the distribution chain, from lead generation and policy issuance through servicing, reconciliation, payouts, compliance, and business intelligence.

Unlike traditional policy administration tools or point-solution workflow software, Fuse-OS serves insurers, brokers, corporate agents, aggregators, NBFCs, banks, automotive finance companies, OEMs, and dealer networks through three flagship platforms: Insurance Distribution Management, Revenue & Reporting Management, and Embedded & Group Products Management. The platform is cloud-native and AI-native, multi-tenant and modular, with enterprise security controls, configurable business rules, and integration-ready architecture.

About the Founders

Pankaj Sharma — Co-Founder & CEO. IIT Roorkee (B.Tech ECE). Leadership experience across insurance distribution and strategy, including roles at Angel One, Liberty General Insurance, Pasarpolis Indonesia, HDFC ERGO, and Coverfox, along with prior founding experience.

Prenit Wankhede — Co-Founder & CTO. IIT Kanpur (B.Tech + M.Tech EE). Engineering leadership across insurance technology and financial services, including roles at Liberty General Insurance, Morgan Stanley, and Coverfox, along with prior founding experience as CTO.

Role Summary

We are hiring Backend Developers with a full-stack orientation to help build and maintain Fuse-OS. You will primarily work with Python and Django, contribute to APIs and backend services, and comfortably support light frontend work using HTML, JavaScript, jQuery, and CSS. You will learn from senior engineers while delivering reliable features in a multi-tenant SaaS environment.

Key Responsibilities

·      Develop and maintain backend features using Python and Django

·      Build and enhance REST APIs, data models, business logic, and admin tooling

·      Work with relational databases (PostgreSQL) and background job processing patterns

·      Support full-stack contributions using HTML, JavaScript, jQuery, and CSS where needed

·      Write clean, testable code; participate in code reviews and follow team engineering standards

·      Debug issues across application, data, and service layers with guidance from senior engineers

·      Collaborate with frontend, QA, and product/delivery stakeholders on requirements and delivery quality

·      Contribute to technical documentation and knowledge sharing within the team

Required Skills & Qualifications

·      Hands-on experience with Python and Django (academic projects, internships, or professional work)

·      Working knowledge of PostgreSQL / relational data modeling and ORM usage

·      Comfort with Git, Linux basics, and debugging application behavior from logs

·      Frontend familiarity for full-stack contribution: HTML, JavaScript, jQuery, and CSS

·      Ability to read existing codebases, follow established patterns, and deliver incremental improvements

·      Strong analytical problem-solving skills and attention to detail

·      Good written and verbal communication

Preferred / Nice to Have

·      Familiarity with browser automation using Selenium and Python (big plus)

·      Exposure to Celery/Redis, Django REST Framework, or common data-processing libraries

·      Basic AWS familiarity (e.g., compute, storage, monitoring) — optional but good to have

·      Experience with SaaS products, multi-tenant applications, or enterprise web systems

Domain Preference

Familiarity with the insurance industry and InsurTech is preferred but not mandatory. Strong engineering fundamentals and willingness to learn the domain matter more.

What We Offer

·      Opportunity to build a category-defining Insurance Distribution Operating System (Fuse-OS)

·      Work on modern multi-tenant SaaS architecture with meaningful ownership and mentoring

·      Collaborative product and engineering culture focused on quality and customer outcomes

·      Exposure to enterprise SaaS, AI-enabled workflows, and large-scale operational systems

·      Growth path with clear impact on product and customers

Are you interested in writing agentic systems that helps companies like Coca-Cola, ITC and Lenovo drive E-commerce success? Do you want to bring Autonomy to E-commerce? Then read on and apply.

Applied Scientist - Decision AI

Location: Bengaluru

Work Schedule: Hybrid (Candidate must be based in Bengaluru - 1-2 days of WFO may be required at a later date)

About Kily

Kily is an AI company bringing autonomy to digital commerce growth. We build autonomous agents that manage Advertising, Pricing and Listings for brands and sellers across commerce marketplaces.

Performance in modern commerce shifts constantly - across marketplaces, categories and cities - faster than teams can manually track, diagnose and act on. Kily's agents work continuously against real business objectives with each brands unique context, objectives and operating constraints and keeping humans in the loop where it matters.

The Role

We are looking for an Applied Scientist to build the models and decision systems behind Kily's recommendations and actions. The work is grounded in messy, real-world commerce data help build Kily's core decision intelligence layer: systems capable of understanding complex commerce data, determining why performance is changing, deciding what should be done about it, and ultimately taking actions autonomously at scale. You will work at the intersection of learning algorithms, decision making under uncertainty and agentic systems.

What You'll Do

·      Conduct deep analysis of commerce data to derive insights, and identify gaps and new opportunities

·      Develop scalable and effective machine-learning models and optimisation strategies to solve business problems across advertising, pricing and listings

·      Define and lead science initiatives from problem framing through production deployment in a high-ambiguity environment

·      Identify and build the sequential feedback loops that make decisions improve over time

·      Design evaluation frameworks to measure the quality and business impact at scale

·      Work closely with engineering, analytics and product teams to take models from experimentation into production

What We're Looking For

·      4+ years in Applied ML/AI, Data Science. Masters or PhD in a quantitative field is a plus

·      Deep proficiency in Python, SQL, statistics and data analysis

·      Hands-on experience developing, deploying and maintaining the end-to-end lifecycle of machine-learning models

·      Experience with LLMs, fine-tuning, AI agents, optimisation or sequential decision systems is a strong plus

·      Exposure to ecommerce, marketplaces, advertising or pricing data is valuable but not essential

·      Strong problem-solving and communication skills; ML research experience is a plus

WHY KILY

Kily is already working with leading brands including ITC, Unilever, Mondelez, Coca-Cola and Lenovo, and has recently raised an INR 30 crore ($3.1 mn) Seed round led by Sorin Investments, with participation from Razorpay and Wyser Capital. You'll have the opportunity to build a foundational AI system from an early stage - one designed not merely to generate insights, but to autonomously drive real-world business

outcomes.

Senior Backend Engineer

What You'll Do :

- Build and scale the core backend and infrastructure powering a customer feedback intelligence platform - owning systems from data ingestion through real-time analytics.

- Harden ML and NLP infrastructure, including pipelines, model serving, and data flows, so features remain reliable in real-world production environments at scale.

- Own critical infrastructure decisions around data models, event flows, and service boundaries, with strong correctness and data-integrity guarantees.

- Instrument systems deeply for observability, ensuring failures surface early and production behavior becomes a source of continuous learning.

- Balance speed, cost, and long-term system health, making pragmatic, cost-effective trade-offs while shipping continuously.

- Partner closely with Product, Design, and the founding team to turn ambiguous problem statements into clear, scalable infrastructure.

- Raise the engineering bar through clean code, thorough design reviews, mentorship, and platform-level improvements.

What It Takes :

- 6-8 years of software engineering experience, with a proven track record of building and shipping reliable systems in production.

- 3+ years at a single organization where you've built infrastructure from the ground up and owned complex projects end to end.

- Deep expertise in infrastructure, including data pipelines, event-driven and microservices architectures, observability, reliability, and cost optimization.

Tech Stack :

-serverless computing, SQL/NoSQL databases, Elasticsearch, or GraphQL.

Bonus :

- Experience building or operating ML/NLP infrastructure such as data pipelines, model serving, feature stores, or inference systems.

Why This Opportunity :

- High Impact : Build foundational backend and infrastructure systems at an early-stage, high-growth technology company.

- Ownership : Take end-to-end responsibility for critical features, systems, and infrastructure.

- Complex Challenges : Work across ML/NLP infrastructure, distributed systems, data pipelines, and real-time analytics.

- Growth : Deepen your technical expertise while having the opportunity to grow into a technical leadership path.

- Culture : Work in an open, collaborative, and values-driven environment with significant autonomy.

- Benefits : Competitive compensation, equity, hybrid work setup, premium healthcare, and more.

About the Role

You'll be at the forefront of designing and implementing robust data platform solutions that power advanced analytics, AI, and machine learning. Working with modern cloud technologies, you'll build scalable data foundations that enable clients to make smarter, data-driven decisions.

Key Responsibilities

• Build scalable data pipelines using Snowflake, AWS, GCP, and Databricks.
• Design and optimize data models for AI and machine learning workloads.
• Develop reliable data foundations for MLOps, governance, and data lineage.
• Integrate data from multiple sources into modern data platforms.
• Leverage Snowpark ML and Snowflake's native AI capabilities.
• Ensure data platforms are secure, scalable, and high-performing.

What We're Looking For

• 3+ years of hands-on experience
• Strong proficiency in SQL and Python.
• Experience with AWS, Azure, or GCP.
• Knowledge of cloud storage services such as S3, ADLS, or GCS.
• Strong understanding of Dimensional Modeling and Data Vault.
• Experience with Scala or Java is a plus.

Tech Stack

• Data Warehouse: Snowflake
• Programming: SQL, Python, Scala (Good to Have), Java (Good to Have)
• Cloud: AWS, Azure, GCP
• Storage: S3, ADLS, GCS
• AI/ML: Snowpark ML, MLOps

Perks & Benefits

• Public Speaking & Communication Program
• Mentoring Program with Senior Support Leads
• 360° Progress Reviews
• Weekly Learning Sessions & Guilds
• Paid Certifications
• Hackathons & Innovation Days
• Recognition & Rewards Programs
• Team Socials & Annual Offsites
• Employee Assistance Program (24/7 Wellbeing Support)

The Data People Shaping Tomorrow

Our client helps organizations unlock the power of data through modern cloud, analytics, and AI solutions. We believe in creating an environment where talented technologists can learn, innovate, and make a real impact while building cutting-edge data platforms for global clients. If you're passionate about data engineering and want to work with the latest technologies in AI, cloud, and analytics, we'd love to hear from you.

Location – Hyderabad (Hybrid)

Work Experience – 5 to 7 years

CTC – upto 20 LPA

Roles & Responsibilities:

· We are looking for a Senior Data Engineering who will be majorly responsible for designing, building and maintaining ETL/ ELT pipelines.

· Integration of data from multiple sources or vendors to provide the holistic insights from data.

· You are expected to build and manage Data warehouse solutions, designing data models, creating ETL processes, implementing data quality mechanisms etc.

· Performs EDA (exploratory data analysis) required to troubleshoot data related issues and assist in the resolution of data issues.

· Should have experience in client interaction.

· Experience in mentoring juniors and providing required guidance.

Required Technical Skills

· Extensive hands on experience in Python, Pyspark, SQL, Dataiku.

· Strong experience in Data Warehouse, ETL, Data Modelling, building ETL Pipelines, Snowflake database.

· Working knowledge in Databricks, Redshift, ADF etc.

· Hands-on experience in cloud services like Azure, AWS- S3, Glue, Lambda, CloudWatch, Athena.

· Sound knowledge in end-to-end Data management, Data ops, quality and data governance.

· Familiar with SFDC, Waterfall/ Agile methodology.

· Strong domain knowledge in Pharma domain/ life sciences commercial data operations.

Qualifications

· Bachelor’s or master’s Engineering/ MCA or equivalent degree.

· 5-7 years of relevant industry experience as Data Engineer.

· Experience working on Pharma syndicated data such as IQVIA, Veeva, Symphony; Claims, CRM, Sales etc.

· High motivation, good work ethic, maturity, self-organized and personal initiative.

· Ability to work collaboratively and providing the support to the team.

· Excellent written and verbal communication skills.

· Strong analytical and problem-solving skills. 

Location: Pune / Gurgaon

Position: AI Engineer

work mode: WFO

  Job Description.

​

 Job responsibilities:

• Responsibility for design, implementation and deployment of Generative AI, Agentic frameworks at scale
• Strong in programming - Python a
• Previous experience of working on Computer Vision projects and VLM /VLAM models.
• In depth awareness of Transformer architectures and End to End Deep neural networks
• Full stack AI / ML development experience
• Design, build & maintain efficient and reliable Agentic / Generative AI code leveraging pipelines
• Hosting and deployment knowledge in GCP or AWS or Azure along with advanced engineering concepts to build user friendly UI interface for easy adoption.

    Requirements:

 ·      4 to 8 years overall years of experience (Agentic AI, Generative AI, VLM, VLAM and LLM) with significant exposure in Development, Architecture design, scaling and hosting in cloud.

    Must Have –

 ·      Architecting and solutioning experience with Python and FAST API, Agentic Ai frameworks, VLMs, VLAMs, Open source LLM’s and Code based LLM models at scale with - Langchain /      Ollama, embeddings, Memory      Management etc.,

·      Practical experience in implementing Explainable and ethical AI models  Practical experience in implementing frameworks like RAG/ CAG/ Self-reflective RAG etc.,

·      Experience in cloud hosting either AWS or Azure or GCP.

·      Experience in ML-OPS - Implement a feedback mechanism to continually improve the model over time through feedback loop and monitoring KPI’s in production.

·      Experience with Quantization and Kubernetes or docker

    Good to have

·      gRPC implementation to expose the API’s on a server for easy usage and good user interface

·      Streamlit front end creation

·      Experience with SAFe framework deliveries.

Passionate about education? Join us at CK-12 !!

CK-12 (www.ck12.org) is on the lookout for talented, creative, and dedicated people to join our mission to provide great education to students around the world. We are looking for candidates to join our office in Bangalore.

We have a strong education platform that has served over 352+ Million users, have got over 2.88+ Billion questions answered, and have more than 350 thousand customized Flexbooks. We have embarked on an exciting journey to build an AI-powered student tutor and Teacher Assistant to build the next generation of learning platforms.

About CK-12 Foundation

CK-12’s mission is to provide free access to open-source content and technology tools that empower students as well as teachers to enhance and experiment with different learning styles, resources, levels of competence, and circumstances.

To achieve this noble and ambitious vision, we at CK-12 are challenging the traditional model of education to transform it dramatically. Technology has opened up lots of opportunities to revolutionize education for the benefit of students, teachers and parents.

We have chosen to be non-profit so that we can effectively realize our mission and do the right thing! It also provides us with the ability to experiment with big and bold ideas. CK-12 is backed by Vinod Khosla, a renowned technology venture capitalist.

At CK-12, you’ll experience the benefits of working in a dynamic, entrepreneurial, innovative and non-bureaucratic environment where you will get a lot of cool things done than you ever imagined! We are a small group of passionate folks who are determined to disrupt the current form of education.

Technology is key to scale education and we deeply believe in it. Come develop great solutions on our cloud-based (AWS) and AI-first platform delivering rich and interactive content.

Does our mission, people and technologies excite you? If the answer is YES! and you are a great technologist who will challenge status-quo (no order takers please!) by innovating, please come join us! Together, we will change the world!

Check out our latest product offerings

• Introducing Flexi 2.0
• Flexi, our AI-powered Student Tutor – https://www.flexi.org/
• AI-powered Teacher Assistant – https://www.ck12.org/pages/teacher-assistant/

Location: https://goo.gl/maps/NkA2Hr8JhtE3raWr5

Backend Engineer

Basic

● Design, maintain, and monitor infrastructure for data products

● Design and develop RESTful APIs for the data infrastructure

● Design, implement and drive adoption of new analytic technologies and solutions

● Work closely with data scientists, front end engineers and peers to gather requirements and

develop solutions

● Handle and resolve issues escalated from the production operational environment.

● Troubleshoot performance, reliability, and scalability issues.

● Excellent Problem solving skills

● A Code Craftsman that follows best software development and coding practices delivering

understandable and maintainable code with thorough unit tests coverage

Requirements

● 10+ years of development experience with Java, Scala, and/or Python

● Experience with writing and executing queries on RDBMS and NoSQL databases. Working knowledge of MongoDB, MySQL. We are not looking for a DBA.

● Experience with ElasticSearch, OpenSearch, Vector Embeddings, and large-scale search systems.

● Experience designing and operating high-throughput, low-latency backend services in production environments.

● Experience with horizontal and vertical scaling strategies, load balancing, caching, rate

limiting, and performance optimization.

● Experience identifying and resolving application bottlenecks through profiling, monitoring, and performance tuning.

● Experience in virtualization technologies and deployment frameworks (familiarity with working on UNIX shell, Jenkins.)

● Bachelors or masters degree in computer science or equivalent

Desired

● Experience working with FastAPI/Pylons Web Framework.

● Familiarity with messaging and distributed processing systems such as RabbitMQ, Kafka, SQS, or Celery.

● Experience designing systems to support millions of requests, large datasets, and high-concurrency workloads.

● Adept at AI Coding assistants, leveraging them beyond prompting. Working experience in Skill and tool development and usage in AI Coding tools.

● Experience with AWS EC2, Redshift, RDS, S3

Job Title : Senior Consultant – Full Stack Developer with AI

Experience : 5+ Years

Open Positions : 1

Location : Remote

Working Hours : 11:00 AM to 08:00 PM IST

Engagement : 6 to 8 Months Contract-to-Hire (C2H), with potential conversion to client payroll

Expected Joining : By the last week of August / 1st week of September

Role Overview :

Thoughtworks is looking for a Senior Consultant – Full Stack Developer with AI experience who can design and develop scalable full-stack applications while leveraging modern AI development tools and agentic AI capabilities.

The ideal candidate should have strong hands-on experience with Python, JavaScript / React.js, AWS Bedrock Agent Core, Docker, Kubernetes, and modern AI-assisted development frameworks and tools. Experience building MCP servers / tools, AI skills, or integrations using tools such as Cursor, Claude Code, Codex, or GitHub Copilot will be highly valuable.

The candidate should be comfortable working across application development, AI integration, cloud technologies, and containerized environments.

Mandatory Skills :

Python, React.js / JavaScript, AWS Bedrock Agent Core, Docker, Kubernetes, MCP / AI Skills, Cursor / Claude Code / Codex / GitHub Copilot, Full-Stack Development.

Key Responsibilities :

• Design, develop, and maintain scalable full-stack applications using modern development practices.
• Build backend services and APIs using Python and related frameworks.
• Develop responsive and scalable frontend applications using ReactJS or other JavaScript frameworks.
• Design and implement AI-powered solutions using AWS Bedrock Agent Core.
• Build and integrate AI agents, tools, skills, and workflows into enterprise applications.
• Develop and work with MCP (Model Context Protocol) servers, tools, or integrations.
• Leverage AI-assisted development platforms and coding tools such as Cursor, Claude Code, Codex, or GitHub Copilot.
• Containerize applications and services using Docker.
• Deploy, manage, and troubleshoot containerized workloads using Kubernetes.
• Collaborate with cross-functional teams to understand business requirements and translate them into scalable technical solutions.
• Follow engineering best practices around code quality, testing, security, performance, and maintainability.
• Contribute to CI/CD and cloud deployment processes; DevOps experience will be an added advantage.
• Participate in technical discussions, architecture decisions, code reviews, and project delivery.

Mandatory Skills :

• 5+ years of relevant software development experience.
• Strong hands-on experience with Python.
• Strong experience with ReactJS or another modern JavaScript framework.
• Hands-on experience with AWS Bedrock Agent Core.
• Strong experience with Docker.
• Hands-on experience with Kubernetes.
• Experience building MCP servers / tools, AI skills, or similar AI integrations.
• Experience using AI-assisted coding/development tools such as :
• Cursor
• Claude Code
• Codex
• GitHub Copilot
• Strong understanding of full-stack application development.
• Good understanding of API development, application architecture, and cloud-based solutions.

Nice to Have :

• Experience with DevOps practices and CI/CD pipelines.
• Experience with AWS cloud services beyond Bedrock.
• Experience with infrastructure automation and deployment.
• Experience building production-grade GenAI / Agentic AI applications.
• Experience with LLM integrations, AI agents, tools, and function calling.

Project Expectations :

Candidates should be able to explain at least one recent project in detail, including :

• Problem Statement : What business / technical problem were you solving ?
• Architecture & Approach : How did you design the solution ?
• Key Contributions : What did you personally build or own ?
• AI / Agent Implementation : How did you use AWS Bedrock Agent Core, MCP, or AI development tools ?
• Technology Stack : Python, React.js / JavaScript, AWS, Docker, Kubernetes, etc.
• Challenges : What were the major technical challenges ?
• Outcomes & Metrics : What measurable impact did the solution deliver, such as performance improvement, cost reduction, automation, productivity improvement, or reduced development time ?

Interview Process :

1. Round 1 – GT Technical Interview : 60 minutes
2. Round 2 – Client Technical Interview : 60 minutes
3. Round 3 – Project Round : 60 minutes
4. Additional Client Round : May be conducted on a case-by-case basis

Key Hiring Priorities :

Highest priority : Candidates with genuine hands-on experience in AWS Bedrock Agent Core + Python + React / JavaScript + Docker + Kubernetes + MCP / AI skills and practical experience using modern AI coding/agent development tools.

Note : Candidates should demonstrate hands-on implementation experience rather than only theoretical knowledge or exposure to the above technologies.