50+ Machine Learning (ML) Jobs in Bangalore (Bengaluru) | Machine Learning (ML) Job openings in Bangalore (Bengaluru)

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

About the Role We are seeking a highly technical, hands-on Senior AI/ML Tech Lead to drive the design, development, and deployment of cutting-edge Generative AI applications. In this dual-impact role, you wi l act as a primary individual contributor architecting core AI engines while simultaneously leading a team of engineers through task alocation, code reviews, and technical mentorship. The ideal candidate bridges the gap between state-of-the-art AI research (LLMs, Agentic frameworks, Advanced RAG, OCR) and production-grade ful-stack engineering (Python, FastAPI, React).

Key Responsibilities

Technical Leadership & Team Management (40%)

● Technical Oversight: Lead a team of AI, backend, and ful-stack engineers; alocate tasks, establish sprint priorities, and ensure timely delivery.

● Code Quality & Reviews: Conduct rigorous code reviews to maintain high engineering standards, security, performance, and scalability across AI and fu l-stack codebases.

● Architecture & Governance: Design end-to-end system architectures for AI solutions, ensuring seamless integration between frontend interfaces, backend APIs, and AI models.

● Mentorship: Guide and upskil team members on modern software practices, LLM engineering, and agentic design patterns. Hands-On Engineering & Development (60%)

● Generative AI & Agentic Systems: Architect, build, and optimize LLM-powered applications, multi-agent workflows (e.g., CrewAI, AutoGen, LangGraph), and autonomous AI agents.

● RAG & OCR Pipelines: Design and deploy advanced RAG (Retrieval-Augmented Generation) architectures and document processing pipelines utilizing OCR techniques (e.g., LayoutLM, PaddleOCR, Tesseract, Vision LLMs) to extract structured data from unstructured sources.

● Backend Systems: Build robust, asynchronous, high-throughput microservices and RESTful APIs using Python and FastAPI.

● Frontend Integration: Colaborate on or build modern web interfaces using React (e.g., Control Towers, operations dashboards, interactive chat interfaces).

● MLOps & Vector DBs: Oversee model deployment, prompt engineering, fine-tuning, vector database integration (Pinecone, Qdrant, Chroma, PGVector), and cloud infrastructure setup (Azure/AWS).

Required Qualifications & Skills

● Overall Experience: 8 to 10 years of professional software engineering experience.

● AI/ML Domain Experience: 3 to 4+ years of dedicated, hands-on experience building and deploying AI/ML, OCR, and Generative AI solutions in production.

● Core Technical Stack: ○ Generative AI & LLMs: Extensive experience with commercial and open-source LLMs (OpenAI, Anthropic Claude, Llama), Agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI), and LLM evaluation frameworks (LangSmith, TruLens, Ragas). ○ RAG & Unstructured Data: Strong knowledge of hybrid search, re-ranking, chunking strategies, vector databases, and document inte ligence workflows. ○ OCR & Vision Techniques: Hands-on experience with OCR engines (Tesseract, PaddleOCR, Azure Document Inteligence) and Multi-Modal/Vision LLMs for document extraction. ○ Backend: Deep expertise in Python and asynchronous frameworks (FastAPI, AsyncIO). ○ Frontend: Working proficiency in React (TypeScript/JavaScript) for building interactive web UI components. ○ Cloud & DevOps: Hands-on experience with cloud platforms (Azure / AWS), Docker, Kubernetes, and CI/CD pipelines.

Preferred / Good-to-Have Skills

● Experience with cloud-native data platforms (e.g., Microsoft Fabric, Snowflake, Azure SQL).

● Familiarity with cost optimization and latency reduction techniques for LLM inference (caching, semantic routing, model quantization).

● Prior experience in client-facing technical leadership or agile consulting environments.

What We Offer

● Opportunity to lead and build high-impact, state-of-the-art Generative AI systems.

● Colaborative engineering culture with room for technical ownership and direct business impact.

● Flexible work arrangements and competitive compensation package.

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

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

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

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.

What you'll do

The Software Engineer will serve as a full stack developer within the AI and Automation team, focusing on building AI agents, chatbots, and automation solutions. The role involves taking ownership and accountability to meet commitments, developing software using programming languages, and designing, executing, and reporting on system and service tests to ensure applications function as required. Additional responsibilities include monitoring, diagnosing, and resolving technology issues, as well as supporting team members by carrying out prescribed design activities using established procedures.

What you'll bring

3+ years of experience in full stack application development using Python (for backend) and JavaScript/Typescript (for frontend).

Strong understanding of Java.

Strong understanding of Machine Learning /MLOps using Python .

Familiarity with Agentic AI frameworks.

Familiarity with Kubernetes (K8S) and Docker.

Experience in developing solutions using Azure storage and compute solutions.

Proven experience working with Azure AI solutions, especially AI Foundry and AI Search.

Experience in developing distributed event-driven applications using brokers such as Apache Kafka, IBM MQ, Solace, or Azure Event Hub.

Proven experience in working with DevOps frameworks, GIT, and CI/CD pipelines.

Experience developing integrations using established Enterprise Application Integration patterns and tools such as Apache Camel.

Required Qualifications

Specify the minimum educational qualifications necessary for this role, including degree type, field of study, and level attained.

Applicants should hold a bachelor’s degree in any discipline and possess practical experience in software development. Degrees in Computer Science, Software Engineering, or Information Technology are particularly advantageous. 

Key Responsibilities

1. Solutioning & Proposal Development

•   Partner with Senior SMEs and Practice Leads to design end-to-end Data & AI solutions for client pursuits — contributing to structure, content, and commercial framing.

•   Build client proposals, solution documents, and program structures that are well-organized, accurate, and ready to use without significant rework.

•   Translate client requirements into structured, outcome-oriented learning journeys — adoption, capability uplift, and measurable business outcomes, not just module lists.

•   Support customized offerings across Data Engineering, AI / ML, and GenAI and Agentic AI tracks; help assemble pursuits from existing accelerators rather than rebuilding from scratch.

2. Client Engagement Support

•   Participate in client discussions, discovery calls, and requirement-gathering sessions — capture context with the rigour that makes the next conversation sharper.

•   Convert business needs into solution frameworks and delivery models with guidance from Senior SMEs; document customer priorities so Practice and Sales can act on them.

•   Support pitch decks, case studies, and success stories — buyer-specific, visually clean, and aligned to how the customer thinks about their own problem.

•   Stay engaged through the proposal cycle and handoff to delivery; ensure no requirement gets lost between discovery and execution.

3. Content & Program Structuring

•   Assist in designing curriculum outlines, learning journeys, and hands-on lab structures that hold up against real-world enterprise contexts.

•   Work with internal and external SMEs to ensure content aligns with current industry trends, real use cases, and business outcomes — not generic technology overviews.

•   Maintain a library of reusable program structures, slide assets, and case study inserts; flag gaps in the existing content library proactively.

4. Research & Market Intelligence

•   Track trends across the AI / GenAI / LLM ecosystem and Data Engineering & Analytics — translate findings into usable inputs for outreach, pitching, and offering design.

•   Identify new solution opportunities and product ideas based on market signals, customer asks, and competitor moves.

•   Benchmark StackRoute's offerings against competitors; surface gaps and differentiation angles for Senior SMEs and Practice Leads to act on.

5. Internal Collaboration

•   Work fluidly with Delivery, Sales, and external SMEs to ensure solutions designed on paper actually work in delivery — surface feasibility risks early, not late.

•   Coordinate inputs across Practice teams during pursuit cycles; hand off to delivery with documentation that captures customer commitments and success metrics.

Must Have Technical & Functional Skills

•   Good understanding of terminologies in Data Engineering (ETL, pipelines, data lakes), Data Analytics & BI concepts, and Machine Learning fundamentals, AI tools ( not technical expertise but L1-L2 knowledge should be present from application standpoint).

•   Awareness of GenAI / LLM vocabulary — prompt engineering, RAG, APIs — with enough depth to hold a credible first conversation with a technical stakeholder.

•   Strong PowerPoint skills (client-ready decks), Excel for effort estimation and costing basics, and structured documentation — proposals, SoWs, one-pagers.

•   Strong problem-solving and structured thinking — breaks complex requirements into clear, communicable solutions.

•   Comfortable communicating with both technical and non-technical stakeholders; good storytelling and presentation instincts; understanding of L&D context is a plus.

Qualifications & Experience

Required:

•   5-15 years in the education products, or in solutioning, pre-sales, or consulting roles with exposure of 3-5 years in Data/AI/Analytics.

•   Bachelor's or Master's in Computer Science, Data Science, Engineering, or a related discipline.

Nice to Have competences:

•   Prior pre-sales, proposal writing, or design development experience.

•   Certifications in cloud, data, or AI platforms (AWS, Azure, GCP, or model-provider certifications).

Core Competencies

·      Structured Thinking: Organises ambiguous client and technical inputs into logical, buyer-relevant narratives. Builds proposals that flow from problem to solution.

·      Solution Articulation: Translates Data & AI capabilities into crisp, persona-specific stories. Adapts the pitch for a CTO, L&D Head, or BU Head without losing substance.

·      Research & Synthesis: Gathers and distils large volumes of information into sharp, usable outputs. Knows what to include and what to leave out.

·      Written Communication: Writes a tight brief, a clean slide, and a clear email. Adapts register from internal working notes to buyer-facing collateral.

·      Curiosity & Learning Agility: Picks up new tools, concepts, and sectors quickly. Tracks AI / GenAI shifts proactively rather than waiting to be told what to read.

·      Bias for Action: Ships a useful 10-slide deck on time rather than a polished 20-slide deck that's late. Comfortable with iteration over perfection.

Key Responsibilities  

• Design, build, and optimize scalable data pipelines for AI/ML applications.
• Develop, train, evaluate, and deploy Machine Learning and Deep Learning models.
• Build production-ready LLM applications using Retrieval-Augmented Generation (RAG), prompt engineering, and vector databases.
• Fine-tune open-source and foundation models using domain-specific datasets.
• Develop and maintain end-to-end MLOps pipelines for model deployment, monitoring, and lifecycle management.
• Perform data preprocessing, feature engineering, exploratory data analysis (EDA), and model evaluation.
• Develop APIs and AI services for production deployment.
• Collaborate with cross-functional teams to deliver scalable AI-driven solutions.
• Monitor model performance, troubleshoot production issues, and maintain technical documentation.

Required Skills  

Mandatory  

• 1–3 years of experience in Data Science, Data Engineering, or AI/ML development.
• Strong programming skills in Python and SQL.
• Hands-on experience with Machine Learning frameworks such as PyTorch, TensorFlow, or Scikit-learn.
• Experience building LLM-powered applications using RAG, Prompt Engineering, and Embeddings.
• Hands-on experience with LangChain, LlamaIndex, CrewAI, or n8n for LLM orchestration and AI workflow automation.
• Experience in LLM fine-tuning and working with Hugging Face models.
• Knowledge of MLOps concepts including model deployment, monitoring, versioning, and CI/CD.
• Experience with Git, REST APIs, Linux environments, and data processing libraries.

Preferred  

• Experience with vector databases such as Pinecone, Chroma, Milvus, or Weaviate.
• Familiarity with Docker, Kubernetes, and MLflow.
• Exposure to Apache Spark or Airflow for data engineering workflows.
• Experience with cloud platforms (AWS, Azure, or GCP).

Primary Technology Stack  

• Languages & Data Processing: Python, SQL, Pandas, NumPy, Apache Spark
• AI & Machine Learning: PyTorch, TensorFlow, Scikit-learn
• Application Frameworks: LangChain, LlamaIndex, CrewAI, n8n
• Core Methodologies: Retrieval-Augmented Generation (RAG), Model Fine-Tuning, Prompt Engineering, Embeddings
• Models & Infrastructure: OpenAI APIs, Hugging Face Ecosystem, Embedding Models
• Vector Databases: Pinecone, Chroma, Milvus, Weaviate
• Databases: PostgreSQL, MongoDB
• MLOps & DevOps: Docker, Kubernetes, MLflow, CI/CD, Git
• Cloud Platforms: AWS, Azure, GCP

Experience: 1–3 Years

Domain: Data Science | Data Engineering | Machine Learning | Generative AI | MLOps

Job Title: AI Architecture Intern

Company: PGAGI Consultancy Pvt. Ltd.

Location: Remote

Employment Type: Internship

Position Overview

We're at the forefront of creating advanced AI systems, from fully autonomous agents that provide intelligent customer interaction to data analysis tools that offer insightful business solutions. We are seeking enthusiastic interns who are passionate about AI and ready to tackle real-world problems using the latest technologies.

Duration: 6 months

Key Responsibilities:

• AI System Architecture Design: Collaborate with the technical team to design robust, scalable, and high-performance AI system architectures aligned with client requirements.
• Client-Focused Solutions: Analyze and interpret client needs to ensure architectural solutions meet expectations while introducing innovation and efficiency.
• Methodology Development: Assist in the formulation and implementation of best practices, methodologies, and frameworks for sustainable AI system development.
• Technology Stack Selection: Support the evaluation and selection of appropriate tools, technologies, and frameworks tailored to project objectives and future scalability.
• Team Collaboration & Learning: Work alongside experienced AI professionals, contributing to projects while enhancing your knowledge through hands-on involvement.

Requirements:

• Strong understanding of AI concepts, machine learning algorithms, and data structures.
• Familiarity with AI development frameworks (e.g., TensorFlow, PyTorch, Keras).
• Proficiency in programming languages such as Python, Java, or C++.
• Demonstrated interest in system architecture, design thinking, and scalable solutions.
• Up-to-date knowledge of AI trends, tools, and technologies.
• Ability to work independently and collaboratively in a remote team environment

Perks:

- Hands-on experience with real AI projects.

- Mentoring from industry experts.

- A collaborative, innovative and flexible work environment

Compensation:

- Stipend: Base is INR 8000/- & can increase up to 20000/- depending upon performance matrix.

After completion of the internship period, there is a chance to get a full-time opportunity as an AI/ML engineer.

Preferred Experience:

• Prior experience in roles such as AI Solution Architect, ML Architect, Data Science Architect, or AI/ML intern.
• Exposure to AI-driven startups or fast-paced technology environments.
• Proven ability to operate in dynamic roles requiring agility, adaptability, and initiative.

Job Title: AI Developer

Location: Bangalore / Hyderabad

Experience: 5–7 Years

Notice Period: Immediate to 15 days Preferred

About the Role

We are looking for a skilled AI Developer to design, develop, and deploy Artificial Intelligence and Machine Learning solutions that solve real-world business problems. You will work closely with data scientists, engineers, and business stakeholders to build scalable AI-powered applications and pipelines.

Key Responsibilities

• Design and develop Machine Learning models, Deep Learning architectures, and NLP solutions for production use.
• Build and maintain end-to-end ML pipelines, from data ingestion and preprocessing to model training, evaluation, and deployment.
• Integrate AI/ML models into existing applications and APIs.
• Fine-tune and evaluate Large Language Models (LLMs) for business-specific use cases.
• Collaborate with data engineers to ensure high-quality training data.
• Monitor model performance in production and implement retraining strategies.
• Research and evaluate emerging AI frameworks, tools, and technologies.
• Document models, experiments, and technical decisions.
• 
  

Required Skills & Experience

• 3+ years of experience in AI/ML development.
• Strong proficiency in Python.
• Hands-on experience with TensorFlow, PyTorch, or Scikit-learn.
• Experience with NLP frameworks such as Hugging Face, spaCy, and LangChain.
• Familiarity with LLMs and Prompt Engineering (OpenAI, Anthropic, Gemini, etc.).
• Experience deploying models using FastAPI or Flask (REST APIs).
• Strong understanding of Data Structures, Algorithms, and Statistics.
• Experience with AWS, Azure, or GCP and MLOps tools.
• Experience with Vector Databases such as Pinecone, Weaviate, or FAISS is an added advantage.

*JD- Lead Developer*

 Seeking an experienced C++/Qt Tech Lead to lead the design and delivery of high-performance, cross-platform software solutions. In this role, you will own technical architecture, drive engineering best practices, and mentor the team while collaborating closely with product and stakeholder groups.

Title: Lead Developer

Location: Bengaluru

Qualifications: Bachelor’s or master's degree in computer engineering or computer science.

Experience 10+ years of experience.

Type: Full-time

Skills Required

Bachelor’s or master's degree in computer science, engineering, or a related field.

10+ years of professional experience in C++ development, including ownership of complex modules or systems.

* Deep expertise in the Qt framework (Qt Widgets) for building robust, cross-platform desktop applications.

 Strong understanding of OOP principles, design patterns, and system design; able to guide design reviews and trade-off decisions.

 Proven experience building multi-threaded, performance-critical applications with a focus on concurrency and stability.

 Strong knowledge of modern C++ and standard libraries; comfortable with writing clean, testable, and maintainable code.

 Hands-on expertise in debugging, profiling, and performance optimization using appropriate tools and techniques.

 Strong problem-solving skills with the ability to take ownership in ambiguous situations and deliver results.

 Excellent communication and leadership skills; able to align teams and stakeholders on technical direction.

Tasks & Activities

Proven, real-world experience architecting or leading healthcare applications, medical imaging, and graphics computing platforms.

 Good understanding of CI/CD concepts, build and release automation, advanced computing, and core data structures.

 Experience working in Agile/Scrum development environments. (must)

 Strong working knowledge of version control systems such as Git (SVN is a plus).

 Demonstrated experience designing systems for imaging datasets and other performance-critical workflows.

 Experience applying AI/ML to medical or dental image analysis.

 Exposure to assisted or automated diagnostic systems is a plus. Training staff / Onsite clients on software use.

 Incorporate new technologies into the products.

 Create technical and regulatory documents for the project.

Location: Mumbai (Work From Office)

About Quantiphi

Quantiphi is an award-winning AI-first digital engineering company driven by the desire to solve transformational business problems. By combining deep industry expertise, cloud and data engineering, and cutting-edge AI research, Quantiphi helps customers build smarter products, frictionless customer experiences, autonomous processes, and safer businesses.

As an Architect – Machine Learning Engineer, you will design and develop advanced machine learning models and algorithms to solve complex business problems. You will optimize and deploy these models on AWS infrastructure, ensuring scalability and reliability.

Must Have Skills

• 7+ years of hands-on experience implementing and developing cloud ML solutions on AWS.
• Strong experience with AWS SageMaker, including:
• Training Jobs
• Processing Jobs
• Batch & Real-time Inference
• Working with multiple data sources
• Strong NLP expertise:
• Deep Learning concepts (Transformers, BERT, Attention Models)
• Python
• Hugging Face Transformers
• SpaCy
• NLTK
• Stanford NLP
• NLP concepts including tokenization, embeddings, syntactic & semantic parsing, Named Entity Recognition (NER), and coreference resolution.
• Experience building Agentic AI applications:
• LangChain
• Amazon Bedrock Agents
• Autonomous task planning and multi-step reasoning
• Experience architecting AI solutions using AWS services:
• AWS Lambda
• Amazon Bedrock
• Step Functions
• S3
• API Gateway
• SageMaker
• Experience implementing Model Context Protocol (MCP) for state, memory, context window, and prompt orchestration.
• Experience integrating agentic workflows with LLMs such as:
• Titan
• Nova
• Cohere
• Claude
• Hands-on experience fine-tuning Large Language Models (LLMs), specifically Llama 2.
• Familiarity with:
• Prompt Engineering
• Tool Calling
• Vector Databases (OpenSearch, Pinecone, Elasticsearch, Bedrock Knowledge Bases)
• Context Management
• Model evaluation and optimization:
• Zero-shot/Few-shot evaluation
• Hyperparameter tuning
• Model interpretability
• Experience with workflow orchestration tools such as:
• Airflow
• AWS Step Functions
• SageMaker Pipelines
• Kubeflow
• Experience building secure, scalable APIs and integrating third-party data sources.
• Strong collaboration skills with Developers, QA, Product Managers, and cross-functional stakeholders.

Good to Have

• Experience working on EdTech use cases.
• Software development experience.

Job Title: Senior AI/ML Researcher – Large Language Models

Location: Bengaluru, India

Experience: Ph.D. or M.Tech (3–4 years research experience post-M.Tech)

Employment Type: Full-time

Company Overview

Big Air Lab (https://www.bigairlab.com/) operates at the edge of applied AI where foundational research meets real-world deployment. We craft intelligent systems that think in teams, adapt with context, and deliver actionable insights across domains.

Position Summary

We’re seeking a Senior AI/ML Researcher who lives and breathes large-scale models, algorithms, and cutting-edge machine learning. If you’re someone who wants to push boundaries in LLMs, predictive modeling, and applied AI research — while also guiding a team to turn theory into real-world solutions — this is your role.

You’re not just an academic — you’re a builder and a mentor. You’ll drive independent research, publish in reputed journals and conferences, and ensure that what’s on paper becomes code, experiments, and scalable AI solutions. You’ll lead from the front — coding, experimenting, mentoring, and showing what world-class research execution looks like.

Key Responsibilities

• Conduct independent research in Large Language Models (LLMs) and related predictive machine learning fields.

• Lead, mentor, and manage a research team comprising of senior members and interns.

• Publish research findings in reputed journals (preferred) or present at leading conferences.

• Develop and experiment with advanced AI and machine learning algorithms.

• Build, validate, and deploy models using PyTorch and other deep learning frameworks.

• Apply rigorous Object-Oriented Programming principles for developing scalable AI solutions.

• Collaborate closely with cross-functional teams to transition research into applicable solutions.

• Provide thought leadership and strategic guidance within the organization.

Required Qualifications

• Ph.D. in Computer Science, AI, or related fields (IIT or top-tier research institute preferred).
• Candidates with Ph.D. require no additional experience.
• M.Tech/MS in Computer Science, AI, or related fields from a top research institute with 3–4 years of dedicated research experience.
• Proven track record of research in LLMs or related AI domains, demonstrated by at least:
• 1 journal publication (preferred), or
• 4 high-quality conference papers.
• Strong expertise in PyTorch and other deep learning frameworks.
• Solid proficiency in Object-Oriented Programming (OOP).

Preferred Attributes

• Detail-oriented with a rigorous analytical mindset.

• Highly capable of conducting and guiding independent research.

• Strong written and verbal communication skills.

• Ability to clearly articulate complex research findings and strategic insights.

Why Join Big Air Lab?

• Lead groundbreaking research in cutting-edge AI technologies.

• Shape the direction of our newly formed R&D department.

• Enjoy significant professional growth, influence, and recognition within the AI research community.

About Impact Analytics

Impact Analytics™ (Series D Funded) delivers AI-native SaaS solutions and consulting services that help companies maximize profitability and customer satisfaction through deeper data insights and predictive analytics. With a fully integrated, end-to-end platform for planning, forecasting, merchandising, pricing, and promotions, Impact Analytics empowers companies to make smarter decisions based on real-time insights rather than relying on last year’s inputs to forecast and plan this year’s business. Powered by over one million machine learning models, Impact Analytics has been leading AI innovation for a decade, setting new benchmarks in forecasting, planning, and operational excellence across the retail, grocery, manufacturing, and CPG sectors. In 2025, Impact Analytics is at the forefront of th eAgentic AI revolution, delivering autonomous solutions that enable businesses to adapt in real time, optimize operations, and drive profitability without manual intervention. Here’s a link to our website: www.impactanalytics.co.

The impact that you will be making

As a senior data scientist, you will help us discover the information hidden in vast amounts of data and help us make smarter decisions to deliver even better products. Primary focus will be in applying data mining techniques, performing statistical analysis, and building high quality prediction systems that can be integrated with our products.

What this role entail

● Understand and translate statistics and analytics to address client business problems.

● Apply Statistical forecasting algorithms to forecast client business needs for short term and long-term horizon.

● Explore Machine learning and Deep learning techniques to improve statistical Forecasting accuracy.

● Create business narrative by using storytelling and Visualization techniques and to present analytical insights to clients.

● Structure business problems and design solutions to meet client needs.

● Develop sophisticated analytical frameworks that add value to the client and result in new projects and revenue streams.

● Develop and implement analytical methodologies, processes, and technological solutions that integrate diverse information solutions and generate analytical insights.

What lands you in this role

● At least 3 years hands-on experience as a data scientist working on SQL, Python

● Must have exposure to developing predictive analytics and machine learning algorithms for business applications

● Strong forecasting and Deep Learning experience will be a plus

● Hands-on experience in relevant tools like SQL, Python, Tableau, etc.

● B Tech/ BE or equivalent degree in Data Science, Statistics, Computer Science, or similar

Some of our accolades include:

● Ranked as one of America's Fastest-Growing Companies by Financial Times for five consecutive years: 2020-2024.

● Ranked as one of America's Fastest-Growing Private Companies by Inc. 5000 for seven consecutive years: 2018-2024.

● Voted #1 by more than 300 retailers worldwide in the RIS Software Leaderboard 2024 report.

● Ranked #72 in America’s Most Innovative Companies list in 2023—by Fortune—alongside companies like Microsoft, Tesla, Apple, IBM, etc.

● Forged a strategic partnership with Google to equip retailers with cutting-edge generative AI tools.

● Recognized in multiple Gartner reports, including Market Guides and Hype Cycle, spanning assortments, merchandising, forecasting, algorithmic retailing, and Unified Price, Promotion, and Markdown Optimization Applications. Economic Times News about our funding can be accessed here.

Looking for somone with strong in AI, who have built the application and scaled them . Start up work exposure

8+ years of experience in successfully building, deploying, and running complex, large-scale web or data products.

Proven Management Experience: Demonstrated success managing a team of 5+ engineers for at least 2 years (managing timelines, performance, and hiring). You know how to transition a team from 'startup chaos' to 'structured agility'. 

● Full-stack Authority: Deep expertise with Javascript, Node.js, MySQL, and Python. You must have world-class expertise in at least one area but possess a solid understanding of the entire stack in a multi-tier environment. 

● Architectural Track Record: Has built at least two professional-grade products as the tech owner/architect and led the delivery of complex products from conception to release. 

● Experience in working with REST APIs, Machine Learning, Algorithms & AWS. 

● Familiar with visualization libraries and database technologies.

 ● Your reputation in the technology community within your domain. 

● Your participation and success in competitive programming. 

● Work on unusual/extraordinary hobby projects during school/college that were not a part of the curriculum. 

● The school that you come from and organizations where you have worked earlier. Personality Expectations We believe that it takes a certain type of personality to do a certain kind of role well. 

● Thoughtful & Analytical: Unlike a sales role, this role requires deep analytical ability and thoughtfulness. You don't just "hit goals at any cost"; you architect sustainable solutions that prevent future debt. 

● The "Pack Leader" Mentality: You are competitive, but you understand that your team's win is your win. You shift from getting a dopamine hit from solving a bug yourself to getting a hit from unblocking your team to solve ten bugs. 

● High Ownership of Outcomes: You don't just care that the code was written; you care that the feature was delivered, works for the customer, and didn't break production. You expect very highly of yourself and being less than ideal anywhere almost pains you. 

● Resilience: You possess the mental endurance to push through complex technical constraints and tight deadlines without losing your cool. 

● Uncompromising Values: On the other side, there is only one thing that we care for apart from performance - your values. We have room for mistakes on the performance side, we have no room for mistakes on your values. 

AI/ML Engineer AI Operating System for Capital Markets Location Bangalore/Chennai Experience 5+ years Function Artificial Intelligence / Machine Learning Employment Type About Transient.AI Full-time Transient.AI is building a next-generation AI Operating System for capital markets — a unified intelligence layer that connects research, trading, compliance, and sales functions at banks and hedge funds. Today, these teams largely operate on disconnected legacy systems, forcing manual, expensive workarounds. Transient.AI replaces that fragmentation with a single AI-native layer built for institutional-grade compliance, security, and auditability. The company already has live products in market, including Caddie.AI (a research automation tool that cuts hedge fund research time significantly), ClarityRIA (helping sales teams identify the right investors in seconds), and CapFlo.AI (automated parsing of complex derivatives contracts). Founded by former traders and technologists from Goldman Sachs, Credit Suisse, UBS, and McKinsey, Transient.AI is headquartered in New York, with teams in Miami, Singapore, and India. The company has raised Series A funding and is scaling its engineering and product organization globally. Role Overview Transient.AI is hiring an experienced AI/ML Engineer to join its India engineering team in Bangalore/Chennai. This is a hands-on, build-from-scratch role — you'll be designing and shipping the core machine learning systems that power the company's flagship products, working closely with founders and senior engineers rather than inheriting existing infrastructure. Key Responsibilities • Design, build, and deploy machine learning and AI models that power Transient.AI's core products (research automation, document intelligence, investor matching, and workflow orchestration). • Workonapplied NLP/LLMsystems, including retrieval-augmented generation, structured extraction from unstructured financial documents, and model evaluation pipelines. • Partner closely with product and founding engineers to translate capital markets workflows into scalable AI systems. • Ownmodelperformance, reliability, and cost — from experimentation through production deployment. • Build and maintain data pipelines, feature stores, and evaluation frameworks to support rapid iteration. • Ensuresystems meet the compliance, auditability, and security standards required in regulated financial environments. What We're Looking For • 5+years ofexperience building and deploying machine learning or AI systems in production.• Strong hands-on experience with Python and modern ML/AI frameworks (PyTorch, TensorFlow, Hugging Face, LangChain, or equivalent). • Experience with LLMs — fine-tuning, prompt engineering, RAG architectures, or agentic systems — is highly valued. • Solid grounding in data structures, distributed systems, and MLOps practices (model serving, monitoring, versioning). • Prior experience at a strong product company, high-growth startup, or a top-tier engineering background • Comfort operating in an early-stage, high-ownership environment with limited process and high ambiguity. • Exposure to fintech, capital markets, or other regulated industries is a plus, though not mandatory. WhyJoin Transient.AI • Build core AI systems from the ground up — not maintain legacy code. • Workdirectly with founders who have deep, first-hand Wall Street experience (Goldman Sachs, Credit Suisse, UBS, McKinsey). • JoinaSeries A-funded company solving a real, expensive problem for institutional finance. • Bepart ofasmall, global team with outsized ownership and impact. .

Machine Learning Engineer (For client company)

Location: Bengaluru, India (Hybrid/Onsite)

Experience: 3–4 years

The Role

We are looking for a Machine Learning Engineer to build and productionize models that power fall detection, vitals monitoring, and predictive health insights from radar sensor data.

You will work closely with hardware, data engineering, backend, and product teams to improve model accuracy, reduce false alarms, and deploy reliable ML systems into production.

This role is ideal for someone with strong classical machine learning fundamentals who is comfortable working with messy real-world sensor data and writing clean, production-grade code.

What You'll Do

• Build and optimize classical ML models such as XGBoost, ensemble models, anomaly detection, and time-series models for fall detection, vitals monitoring, and health risk scoring.
• Engineer features from raw, sparse, and noisy radar signals, point-cloud data, and time-series sensor streams.
• Contribute to computer vision-adjacent problems such as pose estimation, movement analysis, skeleton tracking, and activity recognition using radar data.
• Build training, evaluation, and inference pipelines using Databricks.
• Perform exploratory data analysis on resident, device, alert, and facility-level datasets to identify trends, edge cases, and opportunities for model improvement.
• Define and own model evaluation metrics for safety-critical systems, including:
• Precision
• Recall
• Sensitivity
• Specificity
• False alarm rate
• Missed event rate
• Detection latency
• Analyze production model performance across facilities, residents, devices, and time periods.
• Handle noisy real-world datasets, including:
• Missing values
• Label quality issues
• Device variability
• Sparse event data
• Facility-specific patterns
• Write clean, modular, well-tested Python code for feature engineering, model training, evaluation, and inference.
• Deploy, monitor, and continuously improve production ML models.
• Collaborate with hardware and data engineering teams to improve data quality, labeling, observability, and model reliability.

What We're Looking For

• 3–4 years of experience building and deploying machine learning systems in production.
• Strong Python programming skills with the ability to write maintainable, testable, production-grade code.
• Strong understanding of classical machine learning concepts, including:
• Feature engineering
• Model training
• Cross-validation
• Error analysis
• Model evaluation
• Hands-on experience with algorithms such as:
• XGBoost
• Random Forests
• Gradient Boosting
• Ensemble methods
• Anomaly Detection
• Time-series models
• Strong SQL skills with experience analyzing large datasets using SQL, PySpark, Pandas, or Databricks.
• Experience working with time-series, sensor, spatial, point-cloud, IoT, or computer vision-style datasets.
• Familiarity with modern data engineering workflows using Databricks, Apache Spark, Delta Lake, or similar platforms.
• Strong debugging and analytical skills with the ability to diagnose issues across data pipelines, models, and production systems.
• Comfortable working in a fast-moving startup environment with ambiguity.
• Strong ownership mindset with the ability to take ML models from experimentation through production deployment.

Good to Have

• Experience in HealthTech, IoT, radar sensing, wearables, ambient monitoring, or safety-critical systems.

Exposure to:

• Computer Vision
• Pose Estimation
• Skeleton Tracking
• Object Tracking
• Spatial Data Processing
• Experience with:
• MLflow
• Model Registry
• Feature Stores
• Experiment Tracking
• Model Monitoring
• Experience with:
• ONNX
• Model Quantization
• Edge Deployment
• Latency Optimization
• Resource-Constrained Inference
• Familiarity with real-time data pipelines using:
• Kafka
• Spark Structured Streaming
• Streaming inference architectures 

Mihup is a voice AI company building in-car voice assistance and sound technology for the automotive industry. We power intelligent voice experiences for leading OEMs.

Its solutions integrate automation, real-time assistance, and analytics at scale to enhance accuracy, efficiency, and agility in enterprise operations. Mihup.ai also serves organizations across BFSI, IoT, and other sectors, delivering robust conversation intelligence that supports performance and innovation. With over 1 billion interactions powered, Mihup.ai helps businesses turn voice data into measurable business outcomes and competitive advantage. The company offers a dynamic environment for professionals interested in advanced AI applications in automotive and other industries.

Role Description The AI Engineer will design, develop, and optimize machine learning models and algorithms that power Mihup.ai’s Voice AI and conversation intelligence solutions. Day-to-day responsibilities include building and refining pattern recognition systems, implementing and training neural networks, and developing NLP-based features that improve speech understanding and real-time assistance. The role involves collaborating with product, data, and engineering teams to integrate AI models into production-grade software, monitor performance, and iterate based on user data and business needs. The AI Engineer will also contribute to research, prototyping, and documentation to keep the platform aligned with best practices in AI and scalable software development. This is a full-time, on-site role based in Kolkata/ Bangalore

Qualifications

• Strong foundation in Computer Science, including data structures, algorithms, and software engineering principles.
• B. Tech, M.Tech, PHD from top tier institutes like IIT, IISC, ISI, NIT, IIIT is mandatory.
• Demonstrated experience with Pattern Recognition and Neural Networks for real-world applications.
• Hands-on expertise in Natural Language Processing (NLP), especially in speech, dialogue, or text analytics.
• Proficiency in Software Development using languages such as Python, Java, or similar, and familiarity with version control and CI/CD.
• Knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch) and relevant libraries for NLP and deep learning.
• Experience working with large-scale data pipelines, model evaluation, and performance optimization.
• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related technical discipline, or equivalent practical experience.
• Ability to work collaboratively in cross-functional teams, communicate technical concepts clearly, and document solutions effectively.
• Background in voice technologies, speech recognition, or conversational AI is a strong plus.

Junior Data Scientist (2–3 Years Experience)

• Strong understanding of Probability & Statistics
• Strong educational background in Statistics or Mathematics (degree/course specialization). 
• Knowledge of core Machine Learning algorithms
• Proficiency in Python
• Experience with Natural Language Processing (NLP)
• Understanding of Transformer-based models
• 2–3 years of hands-on Data Science experience
• Good analytical and problem-solving skills

Senior Data Scientist (ML) – 5+ Years

Required Skills:

• 5+ years of experience in Data Science/ML
• Strong knowledge of Probability & Statistics
• Strong educational background in Statistics or Mathematics (degree/course specialization). 
• Good understanding of core Machine Learning algorithms
• Proficiency in Python, PySpark, and SQL
• Experience with Databricks, Azure ML, SageMaker, or Vertex AI
• Hands-on experience in Natural Language Processing (NLP)
• Understanding of Transformer-based models and architectures
• Ability to build, deploy, and optimize ML solutions at scale

Job Description – Data Scientist (Machine Learning & Forecasting)

About the Role

We are looking for a highly skilled Data Scientist with strong expertise in Machine Learning, Traditional Statistical Modelling, Forecasting, and Predictive Analytics. The ideal candidate will have hands-on experience building and deploying end-to-end ML solutions, working with large datasets, and translating business problems into scalable data science solutions.

The role requires a strong foundation in statistics, predictive modelling, feature engineering, model evaluation, and time-series forecasting, along with the ability to collaborate with cross-functional teams to deliver business impact.

Key Responsibilities

• Design, develop, and deploy Machine Learning models for business-critical use cases.
• Build and optimize traditional ML models such as:
• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• Gradient Boosting (XGBoost, LightGBM, CatBoost)
• Support Vector Machines
• Clustering Algorithms
• Develop forecasting solutions using:
• ARIMA / SARIMA
• Prophet
• Exponential Smoothing
• Time-Series Regression Models
• Perform exploratory data analysis (EDA), feature engineering, and data validation.
• Evaluate model performance using appropriate statistical and business metrics.
• Work with structured and semi-structured datasets from multiple sources.
• Collaborate with business stakeholders to understand requirements and translate them into analytical solutions.
• Build scalable data pipelines and support model deployment in production environments.
• Monitor model performance, identify data drift, and implement model retraining strategies.
• Present insights and recommendations to technical and non-technical stakeholders.

Required Skills & Qualifications

• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, or a related quantitative field.
• 5+ years of hands-on experience in Data Science, Machine Learning, and Forecasting.

Technical Skills

   Machine Learning

• Strong understanding of supervised and unsupervised learning algorithms.
• Experience with ensemble methods and advanced ML techniques.
• Expertise in model selection, hyperparameter tuning, and performance optimization.

Forecasting & Statistics

• Strong understanding of:
• Time-Series Analysis
• Forecasting Techniques
• Statistical Inference
• Hypothesis Testing
• Probability Distributions
• A/B Testing

Programming

• Advanced proficiency in Python.
• Experience with:
• Pandas
• NumPy
• Scikit-learn
• Statsmodels
• XGBoost / LightGBM
• Prophet

Data & SQL

• Strong SQL skills with experience in complex queries and performance optimization.
• Experience working with large-scale datasets.

Visualization

• Experience with Power BI, Tableau, Matplotlib, Seaborn, or Plotly.
• Cloud & MLOps (Preferred)
• Exposure to AWS, Azure, or GCP.
• Understanding of Docker, Kubernetes, CI/CD, and ML model deployment practices.

Key Competencies

• Strong analytical and problem-solving skills.
• Excellent communication and stakeholder management abilities.
• Ability to work independently in a fast-paced environment.
• Strong business acumen and data-driven decision-making mindset. 

About the Role

Viamagus is a fully AI-driven engineering organization.

We're hiring a Technical Architect who has built real systems, deployed them to production, and meaningfully integrated AI into their engineering practice. You'll lead architecture across all client engagements.

Must-Have: Engineering Foundation

• Bachelor's in CS/Engineering or related field (Master's preferred)
• 8+ years of software development, 3+ years in an architect/lead role
• Built systems from scratch and taken them to production - owned the full lifecycle, not just slices
• Multiple integration experiences - third-party APIs, enterprise systems (SAP, Salesforce, ERP), messaging/event buses, legacy modernization
• Built frameworks for scalability - reusable platforms, SDKs, shared libraries, and internal developer tooling adopted across teams
• Technology-agnostic strength - strong across at least one modern backend stack, one frontend framework, and one cloud platform; able to pick the right tool for the job rather than defaulting to favourite
• AWS or Azure cloud architecture - VPC design, IAM, container orchestration, cost optimization
• DevOps fluency: Docker, Jenkins/GitHub Actions, IaC (Terraform/CDK)
• Performance tuning, distributed tracing, structured logging, APM tools (Datadog, New Relic, or equivalent)
• AppSec collaboration - OWASP Top 10, VAPT remediation, secrets management, compliance (ISO/SOC 2/HIPAA exposure a plus)

Must-Have: AI-Era Awareness

You will be expected to architect systems that use AI effectively and lead engineers who do the same. Working knowledge of several of these is required:

• AI-assisted development - daily driver of Claude Code, Cursor, Copilot, or equivalent; can articulate where they help, where they fail, and how to get better outcomes from them
• LLM integration patterns - understanding of when to use OpenAI, Anthropic, Gemini, or open-source models; familiarity with API usage, streaming, function calling, structured outputs
• RAG basics — vector DBs (pgvector, Pinecone, Qdrant), embeddings, chunking, retrieval tradeoffs — enough to review and guide RAG implementations
• Agentic systems awareness — conceptual understanding of tool use, multi-step agents, and frameworks like LangGraph or CrewAI
• MCP (Model Context Protocol) — awareness of what it is and where it fits
• Prompt engineering fundamentals — versioning prompts, structured outputs, guardrails, handling hallucinations
• AI evaluation and cost awareness — how to measure quality, latency, and cost of LLM-powered features
• Curiosity and experimentation mindset — has tried things beyond ChatGPT in a browser tab

Responsibilities

Architecture & Delivery

• Design scalable, secure architectures for client engagements
• Lead technical due diligence on proposals - feasibility, effort estimation, risk flagging
• Drive production readiness: incident management, observability, release processes
• Review and approve high-impact design decisions across projects

Team & Stakeholders

• Mentor 15–20 engineers across backend, mobile, and cloud teams
• Conduct architecture reviews, code reviews, and technical retrospectives
• Engage directly with client CTOs/architects on solution design and technical escalations
• Translate business objectives into architectural decisions and vice versa

Quality, Risk, Compliance

• Enforce security-first design - threat modelling, data classification, AI-specific risks (prompt injection, PII leakage, model supply chain)
• Ensure compliance readiness for ISO 27001, SOC 2, HIPAA, where applicable
• Identify and mitigate delivery risks early; escalate with proposed mitigations

Nice to Have

• Contributions to open-source projects or AI tooling
• Experience with real-time sync (CRDTs, Realm, Ditto) or offline-first architectures
• Published technical content - blogs, talks, GitHub
• Google/AWS/Azure certifications (bonus, not substitute)

About Us: Website: https://www.tundratechnical.ca/ & https://tundramanagedsolutions.com/

Experience: 5-8 years

Shift: EST

Location: Bangalore

Mode: Hybrid

Position Requirements

Core (Must-Have) Skills

• Proficiency in Python with advanced use of NumPy, Pandas, and Scikit-learn

• Expertise in SQL, including complex queries and performance optimization

• Strong foundation in machine learning techniques (regression, classification, clustering, ensemble methods)

• Natural Language Processing (NLP), Large Language Models (LLMs), and Generative AI

• MLOps practices (MLFlow, Kubeflow, CI/CD pipelines for ML workflows)

• Experience in data visualization (Seaborn, Plotly, Tableau, or Power BI)

• Applied knowledge of statistics and probability for model development

• Hands-on experience with cloud-based ML platforms (AWS SageMaker, Azure ML, or GCP Vertex AI)

• Exposure to large-scale data processing (Spark, Hadoop)

Preferred Skills

• Deep learning frameworks (TensorFlow, PyTorch)

• Natural Language Processing (NLP), Large Language Models (LLMs), and Generative AI

• MLOps practices (MLFlow, Kubeflow, CI/CD pipelines for ML workflows)

• Data pipeline orchestration (Airflow, Prefect)

• Business domain knowledge (e.g., retail, finance)

Key Activities

• Preparing and preprocessing structured and unstructured data for analysis

• Designing, training, and validating machine learning models

• Deploying and operationalizing models in collaboration with engineering teams

• Building dashboards and data-driven visual insights for stakeholders

• Partnering with product and engineering teams to align models with business needs

• Monitoring, optimizing, and retraining models for performance improvements

We are looking for an experienced Business Analyst with 10+ years of experience to lead AI product initiatives and guide the BA team. The role involves understanding business needs, gathering requirements, creating documents like BRDs, FRDs, and user stories, and coordinating with teams such as engineering, AI/ML, QA, design, and product.

The candidate should have knowledge of AI/ML concepts, strong stakeholder management skills, and experience building products from scratch. They will also mentor junior BAs, conduct UAT testing, support Agile/Scrum processes, and help deliver AI-driven solutions successfully.

Key responsibilities include:

• Requirement gathering and solution analysis
• Stakeholder communication and workshops
• Documentation and project coordination
• UAT and QA support
• Team leadership and mentoring
• Working on AI-powered products and automation solutions
• Ensuring ethical and compliant AI practices

Preferred experience includes exposure to Generative AI, LLMs, AI agents, SaaS products, and tools like Jira, Confluence, Figma, and Miro. 

ML DEVELOPER

Hyperworks Imaging is a cutting-edge technology company based out of Bengaluru, India since 2016. Our team uses the latest advances in deep learning and multi-modal machine learning techniques to solve diverse real world problems. We are rapidly growing, working with multiple companies around the world.

JOB OVERVIEW

We are seeking a talented and results-oriented ML Developer to join our growing team in India. In this role, you will be responsible for developing and implementing new advanced ML algorithms and AI agents for creating AI assistants of the future. 

The ideal candidate will work on a complete ML pipeline starting from extraction, transformation and analysis of data to developing novel ML algorithms. The candidate will implement latest research papers and closely work with various stakeholders to ensure data-driven decisions and integrate the solutions into a robust ML pipeline.

RESPONSIBILITIES:

• Create AI agents using Model Context Protocols (MCPs), Claude Code, DsPy etc.
• Develop custom evals for AI agents.
• Build and maintain ML pipelines
• Optimize and evaluate ML models to ensure accuracy and performance.
• Define system requirements and integrate ML algorithms into cloud based workflows.
• Write clean, well-documented, and maintainable code following best practices

REQUIREMENTS:

• 2-3+ years of experience in data science, machine learning, or a similar role.
• Demonstrated expertise with python, PyTorch, and TensorFlow.
• Graduated/Graduating with B.Tech/M.Tech/PhD degrees in Electrical Engg./Electronics Engg./Computer Science/Maths and Computing/Physics
• Has done coursework in Linear Algebra, Probability, Image Processing, Deep Learning and Machine Learning.
• Has demonstrated experience with Model Context Protocols (MCPs), DSPy, AI Agents, MLOps etc

WHO CAN APPLY:

Only those candidates will be considered who,

• have relevant skills and interests
• can commit full time
• Can show prior work and deployed projects
• can start immediately

Please note that we will reach out to ONLY those applicants who satisfy the criteria listed above.

SALARY DETAILS: Commensurate with experience.

JOINING DATE: Immediate

JOB TYPE: Full-time

Location: Bangalore

Experience: 3-5 years

Type: Full-time | On-site

Start: Immediate

Why this role exists

Most companies are using LLMs.

Very few are building an advantage from them.

Right now, LLM cost is our largest margin constraint, and model behavior is still too generic to be defensible.

This role exists to:

• Turn LLM usage into a cost-efficient system
• Build compounding intelligence across accounts
• Create a differentiated analysis layer that competitors can’t replicate

What you’ll do

You will not just build models.

You will own the intelligence and cost structure of the platform.

1. Drive down LLM cost dramatically

• Reduce cost per interaction from ₹40 → ₹2 within 6 months
• Implement:
• Model tiering (right model for the right task)
• Caching strategies (semantic + response caching)
• Batching and async processing
• PTU / reserved capacity optimization
• Ensure performance does not degrade while reducing cost

2. Optimize infrastructure spend

• Reduce cloud spend from ₹20L/month → ₹4L/month
• Work across infrastructure layers (Azure / compute / inference)
• Balance:
• Latency
• Cost
• Throughput
• Treat infra as a first-class optimization problem

3. Build the fine-tuning and learning pipeline

• Design systems where:
• Every interaction improves future performance
• Build pipelines for:
• Fine-tuning
• Feedback loops
• Continuous model improvement
• Ensure the 5th customer deployment is structurally better than the 1st

4. Create a differentiated intelligence layer

• Build analysis systems that:
• Extract signals from interactions
• Improve decision-making
• Drive outcome improvements
• Move beyond responses → insight + action

5. Enable new AI-native product categories

• Identify opportunities where:
• AI enables workflows that were not previously possible
• Build foundational ML capabilities to unlock those categories
• Focus on creation, not just efficiency

6. Commoditize LLM usage internally

• Abstract complexity of LLM usage from product teams
• Build internal systems where:
• Cost is predictable
• Performance is consistent
• Make LLM usage a reliable utility layer

What success looks like

• Cost per interaction drops to ₹2 or lower
• Infrastructure spend reduces 5x without performance loss
• Model performance improves with every deployment
• Platform develops a clear intelligence advantage
• New AI-native capabilities become possible due to your systems

Who you are

• You have 3-5 years of experience in ML / applied AI / systems engineering
• You have worked with:
• LLMs
• Inference optimization
• Production ML systems
• You think in:
• Systems
• Trade-offs (cost vs latency vs quality)
• You care about real-world impact, not just model metrics

What will make you stand out

• Experience with:
• LLM optimization (prompting, fine-tuning, distillation)
• Distributed systems or infra-level optimizations
• High-scale inference systems
• Built systems that:
• Reduced cost significantly
• Improved performance over time
• Strong understanding of:
• Caching strategies
• Model routing
• Evaluation frameworks

Why join

• You will directly impact company margins and scalability
• Your work defines whether we have a defensible ML advantage
• You will build systems that move from:
• Generic AI usage → compounding intelligence

What this role is not

• Not research-only
• Not experimentation without production impact
• Not isolated from product and business outcomes

What this role is

• A builder of ML systems at scale
• A driver of cost and performance optimization
• A creator of long-term competitive advantage

One question to self-evaluate

Can you build ML systems that get cheaper, smarter, and more valuable with every interaction?

Want to work on exciting GenAI projects for Fortune 500 companies across multiple sectors? Then read on..

About Company:

CSG is a multi-national company having a presence in 20 countries with 1600+ Engineers. Company works with more than 40 Fortune 500 customers such as Sony, Samsung, ABB, Thyssenkrup, Toyota, Mitsubishi and many more.

Job Description:

We are looking for a talented Generative AI Developer to join our dynamic AI/ML team. This position offers an exciting opportunity to leverage cutting-edge Generative AI (GenAI) technologies to drive innovation to solve real world problems. You will be responsible for developing and optimizing GenAI-based applications, implementing advanced techniques like Retrieval-Augmented Generation (RAG), RIG (Retrieval Interleaved Generation), Agentic Frameworks and vector databases. This is a collaborative role where you will work directly with customers cross-functional teams to design, implement, and optimize AI-driven solutions. Exposure to cloud-native AI platforms such as Amazon Bedrock and Microsoft Azure OpenAI is highly desirable.

Key Responsibilities

Generative AI Application Development:

Design, develop, and deploy GenAI-driven applications to address complex industrial challenges.

Implement Retrieval-Augmented Generation (RAG) and Agentic frameworks

Data Management & Optimization:

Design and optimize document chunking strategies tailored to specific datasets and use cases.

Build, manage, and optimize data embeddings for high-performance similarity searches across vector databases.

Collaboration & Integration:

Work closely with data engineers and scientists to integrate AI solutions into existing pipelines.

Collaborate with cross-functional teams to ensure seamless AI implementation.

Cloud & AI Platform Utilization:

Explore and implement best practices for utilizing cloud-native AI platforms, such as Amazon Bedrock and Azure OpenAI, to enhance solution delivery.

Continuous Learning & Innovation:

Stay updated with the latest trends and emerging technologies in the GenAI and AI/ML fields, ensuring our solutions remain cutting-edge.

Requirements:

The ideal candidate will have strong experience in Generative AI technologies, particularly in the areas of RAG, document chunking, and vector database management. They will be able to quickly adapt to evolving AI frameworks and leverage cloud-native platforms to create efficient, scalable solutions. You will be working in a fast-paced and collaborative environment, where innovation and the ability to learn and grow are key to success.

- 3 to 5 years of overall experience in software development, with 3 years focused on AI/ML.

- Minimum 2 years of experience specifically working with Generative AI (GenAI) technologies.

- Python, PySpark and SQL knowledge is necessary for tasks

- Proven ability to work in a collaborative, fast-paced, and innovative environment.

Technical Skills:

- Generative AI Frameworks & Technologies:

- Expertise in Generative AI frameworks, including prompt engineering, fine-tuning, and few-shot learning.

- Familiarity with frameworks such as T5 (Text-to-Text Transfer Transformation), LangChain, Lang Graph, Open-source tech stalk Ollama, Mistral, DeepSeek.

- Strong knowledge of Retrieval-Augmented Generation (RAG) for combining LLMs with external data retrieval systems.

Data Management:

- Experience in designing chunking strategies for different datasets.

- Expertise in data embedding techniques and experience with vector databases like Pinecone, ChromaDB etc

- Programming & AI/ML Libraries:

- Strong programming skills in Python.

- Experience with AI/ML libraries such as TensorFlow, PyTorch, and Hugging Face Transformers.

Cloud Platforms & Integration:

- Familiarity with cloud services for AI/ML workloads (AWS, Azure).

- Experience with API integration for AI services and building scalable applications.

- Certifications (Optional but Desirable):

- Certification in AI/ML (e.g., TensorFlow, AWS Certified Machine Learning Specialty).

- Certification or coursework in Generative AI or related technologies.

What You’ll Be Doing:

• Design and develop advanced AI/ML models to solve complex business problems
• Work closely with cross-functional teams including data engineers and domain experts
• Perform exploratory data analysis, data cleaning, and model development
• Translate business challenges into data-driven solutions and actionable insights
• Drive innovation in advanced analytics and AI/ML capabilities
• Communicate model insights effectively to both technical and non-technical stakeholders

What We’re Looking For:

• 5+ years of experience in AI/ML model development
• Strong foundation in mathematics, probability, and statistics
• Proficiency in Python and exposure to Azure Machine Learning / Databricks
• Experience with supervised & unsupervised learning techniques
• Domain exposure to Energy / Oil & Gas value chain (preferred)
• Strong problem-solving, stakeholder management, and communication skills

Job Title: Software/Hardware Engineer (IIT/NIT)

Location: Bangalore

Website: https://www.zeuron.ai

Experience: 1 Year

CTC: ₹12 LPA

About the Company

Zeuron.ai is a Bangalore-based deep-tech startup founded in 2019, focused on building brain-inspired computing and AI-driven healthcare solutions. The company combines neuroscience, AI, and gaming to create innovative digital therapeutics and neurotechnology platforms for improving brain health, rehabilitation, and overall well-being.

About the Role

We are looking for a highly motivated Software/Hardware Engineer from premier institutes (IIT/NIT) with strong fundamentals and a passion for building scalable and efficient systems. This role offers an opportunity to work on cutting-edge technology and solve real-world problems.

Key Responsibilities

Design, develop, and optimize software/hardware solutions

Work on system architecture, debugging, and performance improvements

Collaborate with cross-functional teams (product, design, operations)

Participate in code reviews, testing, and deployment processes

Contribute to innovation and continuous improvement initiatives

Requirements

B.Tech/M.Tech from IITs/NITs (Computer Science, Electronics, Electrical, or related fields)

1 year of experience (internships/project experience considered)

Strong programming skills (C/C++/Python/Java) or hardware fundamentals (embedded systems, VLSI, circuit design)

Good understanding of data structures, algorithms, and system design

Problem-solving mindset with strong analytical skills

Preferred Skills

Experience with embedded systems, IoT, or product development

Knowledge of cloud platforms or system-level programming

Good in Computer vision, Flutter, JavaScript, AI/ML

🔹 Role: Python Engineer – Python & MLOps

📍 Location: Bellandur, Bangalore

🕐 Work Timings: 01:30 PM – 10:30 PM

🏢 Work Mode: Monday (WFH), Tuesday–Friday (WFO)

📅 Experience: 8-12 Years (Ideal: 8-10 Years)

🔹 Role Overview

This role focuses on building and maintaining a production-grade AI/ML platform. You will work on scalable Python systems, MLOps pipelines, APIs, and CI/CD workflows in an enterprise environment.

🔹 Key Responsibilities

✔ Develop production-grade Python applications using OOP principles

✔ Build and enhance MLOps pipelines (training, validation, deployment)

✔ Design and optimize REST APIs with OpenAI/Swagger

✔ Implement async programming for high-performance systems

✔ Work on CI/CD pipelines (Azure Pipelines / GitHub Actions)

✔ Ensure clean, testable, and maintainable code (PyTest, TDD)

🔹 Required Skills

✔ Strong Python (OOP, modular design)

✔ MLOps & CI/CD pipeline experience

✔ REST API development

✔ Async programming (async/await, concurrency)

✔ Pandas / Polars & Scikit-learn

✔ JSON Schema–driven development

✔ Testing using PyTest

🔹 Nice to Have

➕ Azure ML SDK

➕ Pydantic

➕ Azure Cosmos DB

➕ Experience with large enterprise platforms

This role will report to the Chief Technology Officer

You Will Be Responsible For

* Driving decision-making on enterprise architecture and component-level software design to our software platforms' timely build and delivery.

* Leading a team in building a high-performing and scalable SaaS product.

* Conducting code reviews to maintain code quality and follow best practices

* DevOps practice development on promoting automation, including asset creation, enterprise strategy definition, and training teams

* Developing and building microservices leveraging cloud services

* Working on application security aspects

* Driving innovation within the engineering team, translating product roadmaps into clear development priorities, architectures, and timely release plans to drive business growth.

* Creating a culture of innovation that enables the continued growth of individuals and the company

* Working closely with Product and Business teams to build winning solutions

* Led talent management, including hiring, developing, and retaining a world-class team

Ideal Profile

* You possess a Degree in Engineering or a related field and have at least 20+ years of experience as a Software Engineer, with a 10+ years of experience leading teams and at least 4 Years of experience in building a SaaS / Fintech platform.

* Proficiency in MERN / Java / Full Stack.

* Led a team in optimizing the performance and scalability of a product

* You have extensive experience with DevOps environment and CI/CD practices and can train teams.

* You're a hands-on leader, visionary, and problem solver with a passion for excellence.

* You can work in fast-paced environments and communicate asynchronously with geographically distributed teams.

What's on Offer?

* Exciting opportunity to drive the Engineering efforts of a reputed organisation

* Work alongside & learn from best in class talent

* Competitive compensation + ESOPs

About the Role

We are looking for a passionate AI/ML Software Engineer to join our product team and help build intelligent, scalable, and production-ready machine learning solutions. You will work closely with product managers, designers, and backend engineers to integrate AI capabilities into core product features and drive data-driven innovation.

Key Responsibilities

• Design, develop, and deploy machine learning models into production environments
• Build and maintain scalable ML pipelines for data processing, training, and inference
• Collaborate with cross-functional teams to identify AI-driven product opportunities
• Translate business requirements into ML solutions and technical implementations
• Optimize model performance, accuracy, and latency for real-time applications
• Integrate AI/ML models with APIs and backend systems
• Monitor model performance and implement continuous improvement strategies
• Ensure best practices in data handling, model versioning, and reproducibility
• Stay updated with the latest advancements in AI/ML and apply them to product innovation

Required Skills & Qualifications

• Bachelor’s engineering degree in Computer Science, AI, Data Science, or related field
• Strong programming skills in Python (experience with Java/Go is a plus)
• Hands-on experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn
• Solid understanding of machine learning algorithms and statistical concepts
• Experience in building and deploying ML models in production
• Familiarity with REST APIs, microservices architecture, and cloud platforms (AWS/GCP/Azure)
• Experience with data processing tools (Pandas, NumPy, Spark, etc.)
• Knowledge of version control systems like Git 

Introduction

About Us:

Mercari is a Japan-based C2C marketplace company founded in 2013 with the mission to “Create value in a global marketplace where anyone can buy & sell.” From being the first tech unicorn from Japan before its IPO in 2018 we have come a long way towards becoming a global player and continuously and diligently work towards our transformation journey with a strong focus on our mission.

Since its inception, Mercari Group has worked to grow its services, investing in both our people and technology. Over time Mercari has expanded from being the top player in the C2C marketplace in Japan to new geographies like the U.S. We have also successfully launched new businesses such as Merpay, which is a mobile payment service platform with a vision to create a society where anyone can realize their dreams through a new ecosystem centered not only on payment service but also on credit. Today, Mercari Group is made up of multiple subsidiary businesses including logistics, B2C platform, blockchain, and sports team management.

For our services to be utilized by people worldwide; however, there is still a mountain of work ahead of us. This endeavor naturally requires the capability of the best talent and minds, and that is exactly the reason for us to launch the India Center of Excellence. With your help, we will continue to take on the world stage and strive to grow into a successful global tech company.

Our Culture:

To achieve our mission at Mercari, our organization and each of our employees share the same values and perspectives. Our individual guidelines for action are defined by our four values: Go Bold, All for One, Be a Pro and Move Fast. Our organization is also shaped by our four foundations: Sustainability, Diversity & Inclusion, Trust & Openness, and Well-being for Performance. Regardless of how big Mercari gets, the culture will remain essential to achieving our mission and something we want to preserve throughout our organization. We invite you to read the Mercari Culture Doc which summarizes the behaviors and mindset shared by Mercari and its employees. We continue to build an environment where all of our members of diverse backgrounds are accepted and recognized, and where they can thrive while holding dear to Mercari’s culture.

Work Responsibilities

• Machine learning engineers working in the Recommendation domain develop the functions and services of the marketplace app Mercari through the development and maintenance of machine learning systems like Recommender systems while leveraging necessary infrastructure and companywide platform tools. 
• Mercari is actively applying advanced machine learning technology to provide a more convenient, safer, and more enjoyable marketplace. Machine learning engineers use the cloud and Kubernetes to operate and improve machine learning systems.

Bold Challenges

• We are looking for people who are interested in our services, mission, and values, and want to work where engineers can go bold, use the latest technology, make autonomous decisions, and take on challenges at a rapid pace.
• Develop and optimize machine learning algorithms and models to enhance recommendation system to improve discovery experience of users
• Collaborate with cross-functional teams and product stakeholders to gather requirements, design solutions, and implement features that improve user engagement
• Conduct data analysis and experimentation with large-scale data sets to identify patterns, trends, and insights that drive the refinement of recommendation algorithms
• Utilize machine learning frameworks and libraries to deploy scalable and efficient recommendation solutions.
• Monitor system performance and conduct A/B testing to evaluate the effectiveness of features.
• Continuously research and stay updated on advancements in AI/machine learning techniques and recommend innovative approaches to enhance recommendation capabilities.

Minimum Requirements:

• Over 5-9 years of professional experience in end-to-end development of large-scale ML systems in production
• Strong experience demonstrating development and delivery of end-to-end machine learning solutions starting from experimentation to deploying models, including backend engineering and MLOps, in large scale production systems.
• Experience using common machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn, NumPy, pandas)
• Deep understanding of machine learning and software engineering fundamentals
• Basic knowledge and skills related to monitoring system, logging, and common operations in production environment
• Communication skills to carry out projects in collaboration with multiple teams and stakeholders

Preferred skills:

• Experience developing Recommender systems utilizing large-scale data sets
• Basic knowledge of enterprise search systems and related stacks (e.g. ELK)
• Functional development and bug fixing skills necessary to improve system performance and reliability
• Experience with technology such as Docker and Kubernetes
• Experience with cloud platforms (AWS, GCP, Microsoft Azure, etc.)
• Microservice development and operation experience with Docker and Kubernetes
• Utilizing deep learning models/LLMs in production
• Experience in publications at top-tier peer-reviewed conferences or journals

Employment Status

Full-time

Office

Bangalore

Hybrid workstyle

• We believe in high performance and professionalism. We work from office for 2 days/week and work from home 3 days/week
• To build a strong & highly-engaged organization in India, we highly encourage everyone to work from our Bangalore office, especially during the initial office setup phase
• We will continue to review and update the policy to address future organizational needs

Work Hours

• Full flextime (no core time)

*Flexible to choose working hours other than team common meetings

Media

Owned Media

• Mercari Engineering Portal
• AI at Mercari portal
• Mercan - Introduces the people that make Mercari
• Mercari US Blog

Related Articles

• Development Platforms and Platformers: On Rising to the Global Standard Ken Wakasa, Mercari CTO | mercan
• “I'm Not a Talented Engineer” Insists the Member-Turned-Manager Revamping Our Internal CS Tool | mercan
• Personalize to globalize:How Mercari is reshaping their app, their company, and the world | mercan
• The Providers of the Safe and Secure Mercari Experience: The TnS Team, Introduced by Its Members! | mercan

Job Title : Azure Data Scientist (AI/ML)

Experience : 5 to 10 Years

Location : Bengaluru

Work Mode : Hybrid (4 Days WFO, Tue to Fri – Non-Negotiable)

Notice Period : Immediate Joiner

💡 Role Overview :

We are looking for a highly skilled Azure Data Scientist with strong expertise in AI/ML, Python, and cloud-based data platforms. The role involves building scalable ML solutions, working on GenAI & RAG use cases, and delivering business impact through data-driven insights.

🔥 Mandatory Skills :

Python, Azure Machine Learning, Databricks, AI/ML model development (5+ yrs), Statistics & Probability, EDA & Data Modeling, Machine Learning algorithms, GenAI/RAG experience

✅ Key Responsibilities :

• Design, develop, and deploy AI/ML models to solve complex business problems
• Perform Exploratory Data Analysis (EDA) for data cleaning, discovery, and insights
• Build and optimize ML pipelines using Azure Machine Learning & Databricks
• Work on GenAI applications, RAG implementations, and advanced analytics solutions
• Collaborate with data engineers, business stakeholders, and domain experts
• Translate complex data into actionable business insights
• Manage model lifecycle (development, validation, deployment, monitoring)
• Communicate model outputs and insights to technical & non-technical stakeholders
• Drive innovation and contribute to AI/ML best practices and strategy

🧠 Required Skills (Must Have) :

• Strong experience in Python (ML/AI development)
• Hands-on with Azure Machine Learning & Databricks
• Deep understanding of Mathematics, Probability, and Statistics
• Expertise in Machine Learning & Data Science methodologies
• Experience in EDA, data visualization, and model development
• Exposure to GenAI, RAG, and ML application development
• Minimum 5+ years of experience in AI/ML model development
• Strong problem-solving and analytical skills

➕ Good to Have :

• Experience with MLOps practices
• Domain knowledge in Energy / Oil & Gas value chain
• Experience in data visualization tools
• Team collaboration or mentoring experience

🤝 What We’re Looking For :

• Strong communication & stakeholder management skills
• Ability to work in a cross-functional, global team environment
• Self-driven, adaptable, and innovation-focused mindset

📝 Interview Process :

1. Geektrust Assessment (Assemble)
2. Technical Interview
3. Fitment Round
4. Client Round

Generative AI System Design

• Architect and implement end-to-end LLM-powered applications
• Build scalable RAG pipelines (chunking, embeddings, hybrid search, reranking)
• Design and implement agent-based workflows (tool calling, multi-step reasoning, orchestration)
• Integrate LLM APIs such as OpenAI and Anthropic, along with open-source models
• Implement structured output validation, grounding strategies, and hallucination mitigation
• Optimize inference cost, latency, and token efficiency
• Design evaluation pipelines for performance, accuracy, and safety

2️⃣ Backend & Microservices Engineering

• Design scalable backend systems using Python
• Build REST and async APIs using FastAPI / Django
• Architect and implement microservices with clear service boundaries
• Implement service-to-service communication (REST, gRPC, event-driven messaging)
• Work with message brokers (Kafka / RabbitMQ)
• Optimize database performance (PostgreSQL, MongoDB)
• Implement caching strategies (Redis)
• Build observability: logging, monitoring, distributed tracing

3️⃣ Cloud-Native Architecture & DevOps

• Design and deploy containerized services using Docker
• Orchestrate services using Kubernetes
• Implement CI/CD pipelines
• Ensure system scalability, resilience, and fault tolerance
• Apply distributed systems principles:
• Circuit breakers
• API gateway patterns
• Load balancing
• Horizontal scaling
• Saga patterns
• Zero-downtime deployments

About the Role

We are looking for a highly skilled Data Scientist with strong expertise in Machine Learning, MLOps, and Generative AI. The ideal candidate will have hands-on experience in building scalable ML models, deploying them in production, and working with modern AI frameworks, including GenAI technologies.

Key Responsibilities

·      Design, develop, and deploy machine learning models for real-world business problems

·      Work on end-to-end ML lifecycle: data preprocessing, model building, evaluation, deployment, and monitoring

·      Implement and manage MLOps pipelines for scalable and reproducible workflows

·      Utilize tools like MLflow for experiment tracking, model versioning, and lifecycle management

·      Develop and integrate Generative AI (GenAI) solutions such as LLM-based applications

·      Collaborate with cross-functional teams (engineering, product, business) to translate requirements into AI solutions

·      Optimize model performance and ensure production stability

·      Stay updated with the latest advancements in AI/ML and GenAI ecosystems

Required Skills & Qualifications

·      4+ years of experience in Data Science / Machine Learning

·      Strong programming skills in Python

·      Hands-on experience with ML modeling techniques (supervised, unsupervised, NLP, etc.)

·      Solid understanding of MLOps practices and tools

·      Experience with MLflow or similar model lifecycle tools 

·      Practical experience in Generative AI (GenAI), including working with LLMs

·      Experience with libraries/frameworks like Scikit-learn, TensorFlow, PyTorch

·      Strong understanding of data structures, algorithms, and statistics

·      Experience with cloud platforms (AWS/GCP/Azure) is a plus

Good to Have

·      Experience with LLM fine-tuning, prompt engineering, or RAG pipelines

·      Exposure to Docker, Kubernetes, and CI/CD pipelines

·      Knowledge of data engineering workflows 

• 10+ years of software development experience
• 3+ years in a technical leadership role
• Strong expertise in Python and SQL
• Experience building scalable APIs and backend systems
• Solid understanding of database design and performance tuning
• Experience with Azure cloud services (AWS familiarity preferred)
• Working knowledge of ML/AI integration in enterprise systems
• Experience in client-facing or consulting environments preferred
• Experience with Databricks or modern data platforms
• Exposure to ETL tools such as Talend
• Experience with BI tools (e.g., Power BI)
• Exposure to regulated domains such as Pharma, Healthcare

🤖 Data Scientist – Frontier AI for Data Platforms & Distributed Systems (4–8 Years)

Experience: 4–8 Years

Location: Bengaluru (On-site / Hybrid)

Company: Publicly Listed, Global Product Platform

🧠 About the Mission

We are building a Top 1% AI-Native Engineering & Data Organization — from first principles.

This is not incremental improvement.

This is a full-stack transformation of a large-scale enterprise into an AI-native data platform company.

We are re-architecting:

• Legacy systems → AI-native architectures
• Static pipelines → autonomous, self-healing systems
• Data platforms → intelligent, learning systems
• Software workflows → agentic execution layers

This is the kind of shift you would expect from companies like Google or Microsoft —

Except here, you will build it from day zero and scale it globally.

🧠 The Opportunity: This role sits at the intersection of three high-impact domains:

1. Frontier AI Systems: Large Language Models (LLMs), Small Language Models (SLMs), and Agentic AI

2. Data Platforms: Warehouses, Lakehouses, Streaming Systems, Query Engines

3. Distributed Systems: High-throughput, low-latency, multi-region infrastructure

We are building systems where:

• Data platforms optimize themselves using ML/LLMs
• Pipelines are autonomous, self-healing, and adaptive
• Queries are generated, optimized, and executed intelligently
• Infrastructure learns from usage and evolves continuously

This is: AI as the control plane for data infrastructure

🧩 What You’ll Work On

You will design and build AI-native systems deeply embedded inside data infrastructure.

1. AI-Native Data Platforms

• Build LLM-powered interfaces:
• Natural language → SQL / pipelines / transformations
• Design semantic data layers:
• Embeddings, vector search, knowledge graphs
• Develop AI copilots:
• For data engineers, analysts, and platform users

2. Autonomous Data Pipelines

• Build self-healing ETL/ELT systems using AI agents
• Create pipelines that:
• Detect anomalies in real time
• Automatically debug failures
• Dynamically optimize transformations

3. Intelligent Query & Compute Optimization

• Apply ML/LLMs to:
• Query planning and execution
• Cost-based optimization using learned models
• Workload prediction and scheduling
• Build systems that:
• Learn from query patterns
• Continuously improve performance and cost efficiency

4. Distributed Data + AI Infrastructure

• Architect systems operating at:
• Billions of events per day
• Petabyte-scale data
• Work with:
• Distributed compute engines (Spark / Flink / Ray class systems)
• Streaming systems (Kafka-class infra)
• Vector databases and hybrid retrieval systems

5. Learning Systems & Feedback Loops

• Build closed-loop AI systems:
• Execution → feedback → model updates
• Develop:
• Continual learning pipelines
• Online learning systems for infra optimization
• Experimentation frameworks (A/B, bandits, eval pipelines)

6. LLM & Agentic Systems (Infra-Aware)

• Build agents that understand data systems
• Enable:
• Autonomous pipeline debugging
• Root cause analysis for infra failures
• Intelligent orchestration of data workflows

🧠 What We’re Looking For

Core Foundations

• Strong grounding in:
• Machine Learning, Deep Learning, NLP
• Statistics, optimization, probabilistic systems
• Distributed systems fundamentals
• Deep understanding of:
• Transformer architectures
• Modern LLM ecosystems

Hands-On Expertise

• Experience building:
• LLM / GenAI systems (RAG, fine-tuning, embeddings)
• Data platforms (warehouse, lake, lakehouse architectures)
• Distributed pipelines and compute systems
• Strong programming skills:
• Python (ML/AI stack)
• SQL (deep understanding — query planning, optimization mindset)

Systems Thinking (Critical)

You think in systems, not components.

• Built or worked on:
• Large-scale data pipelines
• High-throughput distributed systems
• Low-latency, high-concurrency architectures
• Understand:
• Query optimization and execution
• Data partitioning, indexing, caching
• Trade-offs in distributed systems

🔥 What Sets You Apart (Top 1%)

• Built AI-powered data platforms or infra systems in production
• Designed or contributed to:
• Query engines / optimizers
• Data observability / lineage systems
• AI-driven infra or AIOps platforms
• Experience with:
• Multi-modal AI (logs, metrics, traces, text)
• Agentic AI systems
• Autonomous infrastructure
• Worked on systems at scale comparable to:
• Google (BigQuery-like systems)
• Meta (real-time analytics infra)
• Snowflake / Databricks (lakehouse architectures)

🧬 Ideal Background (Not Mandatory)

We often see strong candidates from:

• Data infrastructure or platform engineering teams
• AI-first startups or research-driven environments
• High-scale product companies

Experience building:

• Internal platforms used by 1000s of engineers
• Systems serving millions of users / high throughput workloads
• Multi-region, distributed cloud systems

🧠 The Kind of Problems You’ll Solve

• Can LLMs replace traditional query optimizers?
• How do we build self-healing data pipelines at scale?
• Can data systems learn from every query and improve automatically?
• How do we embed reasoning and planning into infrastructure layers?
• What does a fully autonomous data platform look like?

Background: We Commonly See (But Not Limited To)

Our team often includes engineers from top-tier institutions and strong research or product backgrounds, including:

• Leading engineering schools in India and globally
• Engineers with experience in top product companies, AI startups, or research-driven environments
• That said, we care far more about demonstrated ability, depth, and impact than pedigree alone.

🧭 Tech Lead (Backend / Fullstack | 7–10 Years)

Location: Bangalore (On-Site, Hybrid)

Company Type: Public-Listed Product Company

We’re Building a “Top 1% Engineering Org”

We’re building a high-talent-density, AI-first R&D organization from scratch — inside a publicly listed company undergoing a full-scale transformation.

Think:

→ Rewriting legacy systems into AI-native architectures

→ Embedding LLMs + Agentic AI into core workflows

→ Reimagining platforms, infra, and data systems for the next decade

This is the kind of shift you’d expect from Google, Microsoft, or Meta —

Except you get to build it from day 0 → scale it globally.

About the Role / Team

We are building a next-generation AI-first R&D organization in Bengaluru, focused on solving complex problems across LLMs, Agentic AI systems, distributed computing, and enterprise-scale architectures.

This initiative is part of a publicly listed global company investing heavily in AI-driven transformation, re-architecting its platforms into intelligent, autonomous systems powered by large language models, workflows, and decision engines.

You will be working on:

• Agentic AI systems & LLM-powered workflows
• Distributed, scalable backend systems
• Enterprise-grade AI platforms
• Automation-first engineering environments

🚀 The Mandate

Lead execution of mission-critical systems while staying hands-on — bridging architecture and delivery.

🧩 What You’ll Do

• Own end-to-end delivery of complex engineering initiatives (0→1, 1→N)
• Design systems across backend + frontend (if fullstack)
• Translate ambiguous problems into structured technical solutions
• Drive engineering best practices, code quality, and velocity
• Mentor engineers and elevate team performance
• Collaborate with stakeholders on roadmap and execution strategy

🧠 What We’re Looking For

• Strong experience in backend systems + optional frontend frameworks
• Proven ability to lead projects and deliver at scale
• Solid understanding of system design and architecture patterns
• Ability to balance speed vs quality vs scalability trade-offs
• Strong communication and leadership without authority
• Strong coding skills in Python / Java / Go / Node.js
• Solid understanding of data structures, system design basics, and backend architecture
• Experience building scalable APIs and services
• Familiarity or curiosity around AI/LLMs, async systems, or event-driven design
• Strong debugging, problem-solving, and ownership mindset

Nice to Have

• Experience integrating LLMs, vector databases, or AI pipelines
• Contributions to architecture at scale
• Experience with Agentic AI / LLM orchestration frameworks
• Background in product engineering or platform companies
• Exposure to global-scale systems (millions of users / high throughput)

🔥 What Sets You Apart

• Experience leading platform builds or major system rewrites
• Exposure to AI systems, LLM integrations, or intelligent workflows
• Built platforms used by millions of users / high-throughput systems
• Experience with event-driven systems, stream processing, or infra platforms
• Prior work on AI/ML platforms, model serving, or intelligent systems

Background: We Commonly See (But Not Limited To)

• Our team often includes engineers from top-tier institutions and strong research or product company or DeepTech or AI Product backgrounds, including:
• Leading engineering schools in India and globally
• Engineers with experience in top product companies, AI startups, or research-driven environments
• That said, we care far more about demonstrated ability, depth, and impact than pedigree alone.

🚨 We’re Building a “Top 1% Engineering Org”

We’re building a high-talent-density, AI-first R&D organization from scratch — inside a publicly listed company undergoing a full-scale transformation.

Think:

→ Rewriting legacy systems into AI-native architectures

→ Embedding LLMs + Agentic AI into core workflows

→ Reimagining platforms, infra, and data systems for the next decade

This is the kind of shift you’d expect from Google, Microsoft, or Meta —

Except you get to build it from day 0 → scale it globally.

About the Role / Team

We are building a next-generation AI-first R&D organization in Bengaluru, focused on solving complex problems across LLMs, Agentic AI systems, distributed computing, and enterprise-scale architectures.

This initiative is part of a publicly listed global company investing heavily in AI-driven transformation, re-architecting its platforms into intelligent, autonomous systems powered by large language models, workflows, and decision engines.

You will be working on:

• Agentic AI systems & LLM-powered workflows
• Distributed, scalable backend systems
• Enterprise-grade AI platforms
• Automation-first engineering environments

🚀 The Mandate

Own and evolve the technical backbone of an AI-first enterprise platform.

You will define architecture across LLM-powered systems, distributed services, and data platforms — and lead critical transformations from legacy → AI-native systems.

🧩 What You’ll Do

• Architect large-scale distributed systems powering AI-driven workflows
• Lead 0→1 and 1→N platform builds (LLM integrations, agentic systems, orchestration layers)
• Redesign legacy systems into scalable, modular, AI-native architectures
• Drive system design excellence across teams (APIs, infra, observability, reliability)
• Make high-stakes decisions on trade-offs (latency, cost, scalability, model performance)
• Mentor senior engineers and influence engineering culture/org standards
• Partner with product, data, and leadership on long-term technical strategy

🧠 What We’re Looking For

• Proven track record building high-scale backend or platform systems
• Deep expertise in distributed systems, microservices, cloud (AWS/GCP/Azure)
• Strong exposure to data systems/infra / Data / real-time architectures
• Experience or strong interest in LLMs, GenAI, or AI system design
• Exceptional system design, abstraction, and problem-solving ability
• High ownership mindset — you think in terms of systems, not tickets
• Strong coding skills in Python / Java / Go / Node.js
• Solid understanding of data structures, system design basics, and backend architecture
• Experience building scalable APIs and services
• Familiarity or curiosity around AI/LLMs, async systems, or event-driven design
• Strong debugging, problem-solving, and ownership mindset
• Solve hard system problems (latency, scale, reliability)
• Drive cross-team technical decisions and standards
• Mentor senior engineers and influence org-wide architecture 
• Design large-scale distributed systems and backend platforms
• Mentorship & Technical Leadership 
• Expertise in system design, scalability, and performance optimization

Nice to Have

• Experience integrating LLMs, vector databases, or AI pipelines
• Contributions to architecture at scale
• Experience with Agentic AI / LLM orchestration frameworks
• Background in product engineering or platform companies
• Exposure to global-scale systems (millions of users / high throughput)

🔥 What Sets You Apart

• Built platforms used by millions of users / high-throughput systems
• Experience with event-driven systems, stream processing, or infra platforms
• Prior work on AI/ML platforms, model serving, or intelligent systems

Job Details

- Job Title: Director of Engineering

- Industry: SAAS

- Function – Information Technology

- Experience Required: 9-14 years

- Working Days: 6 days

- Employment Type: Full Time

- Job Location: Bangalore

- CTC Range: Best in Industry

Preferred Skills: TypeScript, AWS, NodeJS, mongodb, React.js, WebGL, Three.js, AI/ML, Docker,nKubernetes

Criteria

Candidate must be having 9+ years of engineering experience, with 3u20134 years in technical leadership

Hands-on expertise with React/Next.js, Node.js/Python, and AWS.

Ability to design scalable architectures for high-performance systems.

Should have AI/ML deployment experience

Strong 3D graphics/WebGL/Three.js knowledge.

Candidates should be from SAAS/Software/IT Services based startups or scaleup companies only

Job Description

The Role:

Company is hiring a hands-on Director of Engineering who codes, architects systems, and builds teams. You’ll set the technical foundation, drive engineering excellence, and own the architecture of our AI, 3D, and XR platform.

This is not a pure management role - expect to spend 50–60% of your time writing code, solving deep technical problems, and owning mission-critical systems. As we scale, this role transitions into CTO, taking full ownership of technical vision and long-term strategy. 

What You’ll Own:

1. Technical Leadership & Architecture

● Architect company’s full-stack platform across frontend, backend, infrastructure, and AI.

● Scale core systems: VersaAI engine, rendering pipeline, AR deployment, analytics.

● Make decisions on stack, scalability patterns, architecture, and technical debt.

● Own design for high-performance 3D asset processing, real-time rendering, and ML deployment.

● Lead architectural discussions, design reviews, and set engineering standards.

2. Hands-On Development

● Write production-grade code across frontend, backend, APIs, and cloud infra.

● Build critical features and core system components independently.

● Debug complex systems and optimize performance end-to-end.

● Implement and optimize AI/ML pipelines for 3D generation, CV, and recognition.

● Build scalable backend services for large-scale asset processing and real-time pipelines.

● Develop WebGL/Three.js rendering and AR workflows.

3. Team Building & Engineering Management

● Hire and grow a team of 5–8 engineers initially (scaling to 15–20).

● Establish engineering culture, values, and best practices.

● Build career frameworks, performance systems, and growth plans.

● Conduct 1:1s, mentor engineers, and drive continuous improvement.

● Set up processes for agile execution, deployments, and incident response.

4. Product & Cross-Functional Collaboration

● Work with the founder and product team on roadmap, feasibility, and prioritization.

● Translate product requirements into technical execution plans.

● Collaborate with design for UX quality and technical alignment.

● Support sales and customer success with integrations and technical discussions.

● Contribute technical inputs to product strategy and customer-facing initiatives.

5. Engineering Operations & Infrastructure

● Own CI/CD, testing frameworks, deployments, and automation.

● Create monitoring, logging, and alerting setups for reliability.

● Manage AWS infrastructure with a focus on cost and performance.

● Build internal tools, documentation, and developer workflows.

● Ensure enterprise-grade security, compliance, and reliability.

Tech Stack:

1. Frontend

React.js, Next.js, TypeScript, WebGL, Three.js

2. Backend

Node.js, Python, Express/FastAPI, REST, GraphQL

3. AI/ML

PyTorch, TensorFlow, CV models, Stable Diffusion, LLMs, ML pipelines

4. 3D & Graphics

Three.js, WebGL, Babylon.js, glTF, USDZ, rendering optimization

5. Databases

PostgreSQL, MongoDB, Redis, vector databases

6. Cloud & Infra

AWS (EC2, S3, Lambda, SageMaker), Docker, Kubernetes CI/CD: GitHub Actions

Monitoring: Datadog, Sentry

What We’re Looking For:

1. Must-Haves

● 9+ years of engineering experience, with 3–4 years in technical leadership.

● Deep full-stack experience with strong system design fundamentals.

● Proven success building products from 0→1 in fast-paced environments.

● Hands-on expertise with React/Next.js, Node.js/Python, and AWS.

● Ability to design scalable architectures for high-performance systems.

● Strong people leadership with experience hiring and mentoring teams.

● Ready to code, review, design, and lead from the front.

● Startup mindset: fast execution, problem-solving, ownership.

2. Highly Desirable

● AI/ML deployment experience (CV, generative AI, 3D reconstruction).

● Strong 3D graphics/WebGL/Three.js knowledge.

● Experience with real-time systems, rendering optimizations, or large-scale pipelines.

● Background in B2B SaaS, XR, gaming, or immersive tech.

● Experience scaling engineering teams from 5 → 20+.

● Open-source contributions or technical content creation.

● Experience working closely with founders or executive leadership.

Why Company:

● Hard, meaningful engineering problems at the intersection of AI, 3D, XR, and web tech.

● Build from day zero – architecture, team, and culture.

● Path to CTO as the company scales.

● High autonomy to drive technical decisions.

● Direct founder collaboration on product vision.

● High ownership, high-growth environment.

● Backed by global leaders: Microsoft, Google, NVIDIA, AWS.

Location & Work Culture:

● Location: HSR Layout, Bengaluru

● Schedule: 6 days a week, (5 days-in-office, Saturdays WFH)

● Culture: High-intensity, high-integrity, engineering-first

● Team: Young, ambitious, technically strong

Description

JOB DESCRIPTION – SENIOR ANALYST – DATA SCIENTIST 

Key Responsibilities ·       

Work with business stakeholders and cross-functional SMEs to deeply understand business context and key business questions·       

Advanced skills with statistical/programming in Python and data querying languages (e.g., SQL, Hadoop/Hive, Scala)·       

Solid understanding of time-series forecasting techniques·       

Good hands-on skills in both feature engineering and hyperparameter optimization·       

Able to write clean and tested code that can be maintained by other software engineers·       

Able to clearly summarize and communicate data analysis assumptions and results·       

Able to craft effective data pipelines to transform your analyses from offline to production systems·       

Self-motivated and a proactive problem solver who can work independently and in teams·       

Connects both externally and internally to understand industry trends, technology advances and outstanding processes or solutions·       

Is collaborative and engages (strategic & tactical. Able to influence without authority, handle complex issues and implement positive change·       

Work on multiple pillars of AI including cognitive engineering, conversational bots, and data science·       

Ensure that solutions exhibit high levels of performance, security, scalability, maintainability, repeatability, appropriate reusability, and reliability upon deployment ·       

Provide guidance and leadership to more junior data scientists, managing processes and flow of work, vetting designs, and mentoring team members to realize their full potential·       

Lead discussions at peer review and use interpersonal skills to positively influence decision making·       

Provide subject matter expertise in machine learning techniques, tools, and concepts; make impactful contributions to internal discussions on emerging practices·       

Facilitate cross-geography sharing of new ideas, learnings, and best-practices 

What We Are Looking For 

Required Qualifications ·       

Master's degree in a quantitative field such as Data Science, Statistics, Applied Mathematics or Bachelor's degree in engineering, computer science, or related field. ·       

4 – 6 years of total work experience as data scientist or analytical role, with at least 2-3 years of experience in time series forecasting·       

A combination of business focus, strong analytical and problem-solving skills, and programming knowledge to be able to quickly cycle hypothesis through the discovery phase of a project ·       

Strong experience in Time Series Forecasting and Demand Planning ·       

Advanced skills with statistical/programming software (e.g., R, Python) and data querying languages (e.g., SQL, Hadoop/Hive, Scala) ·       

Good hands-on skills in both feature engineering and hyperparameter optimization ·       

Experience producing high-quality code, tests, documentation·       

Understanding of descriptive and exploratory statistics, predictive modelling, evaluation metrics, decision trees, machine learning algorithms, optimization & forecasting techniques, and / or deep learning methodologies·       

Proficiency in statistical concepts and ML algorithms·       

Ability to lead, manage, build, and deliver customer business results through data scientists or professional services team·       

Ability to share ideas in a compelling manner, to clearly summarize and communicate data analysis assumptions and results·       

Self-motivated and a proactive problem solver who can work independently and in teams·       

Outstanding verbal and written communication skills with the ability to effectively advocate technical solutions to engineering and business teams 

Desired Qualifications ·      

Experience working in one or multiple supply chain functions (e.g., procurement, planning, manufacturing, quality, logistics) is strongly preferred ·       

Experience in applying AI/ML within a CPG or Healthcare business environment is strongly preferred ·       

Experience in creating CI/CD pipelines for deployment using Jenkins. ·       

Experience implementing MLOPs framework along with understanding of data security·       

Implementation on ML models·       

Exposure to visualization packages and Azure tech stack.  

Must have skills

Python - 2 years

Data Science - 4 years

SQL - 2 years

Machine Learning - 2 years

Nice to have skills

Data Analysis - 4 years

Time Series Forecasting - 2 years

Demand Planning - 2 years

Hadoop - 2 years

Statistical concepts - 2 years

Supply chain functions - 2 years

Hi ,

Title                            : Senior AI/ML Engineer

Experience                 : 5 – 10+ Yrs

Location                     : Bengaluru

Work Type                : Hybrid – 2 days Work from office

Type of hire               : PwD & Non-PwD Inclusive Hiring

Employment Type     : Full Time

Notice Period             : Immediate Joiner

Workdays                   : Mon - Fri

Role Overview

We are seeking an exceptional AI Engineer who can design and build production-grade AI systems that combine advanced machine learning, Generative AI, and scalable software engineering.

This role goes beyond traditional data science and focuses on building end-to-end AI platforms, autonomous AI agents, intelligent decision systems, and enterprise AI applications.

You will work on real-world enterprise problems across industries, developing AI systems that automate reasoning, prediction, and decision-making at scale.

What You Will Build

Examples of systems you may work on:

• AI Copilots for enterprise workflows

• Autonomous AI agents for automation

• Decision intelligence platforms

• Retrieval-Augmented Generation (RAG) systems

• Predictive ML systems for forecasting and anomaly detection

• AI-powered knowledge assistants

• Intelligent automation platforms

Key Responsibilities

1. Advanced Machine Learning & Predictive Systems

Design and implement ML models including:

• Time series forecasting

• Predictive modeling

• Anomaly detection

• Recommendation systems

• NLP / text intelligence

• Deep learning models

Develop models using:

• PyTorch

• TensorFlow

• Scikit-learn

• XGBoost / LightGBM

2. Generative AI & LLM Systems

Build enterprise-grade GenAI applications including:

• AI copilots

• conversational agents

• document intelligence systems

• enterprise knowledge assistants

Develop LLM systems using:

• OpenAI / Claude / Gemini / Llama

• prompt engineering techniques

• embeddings and semantic search

• RAG architectures

3. Agentic AI Systems

Design autonomous AI systems capable of reasoning and executing tasks.

Build multi-agent architectures using:

• LangGraph

• CrewAI

• AutoGen

• Semantic Kernel

Integrate agents with:

• APIs

• enterprise data systems

• internal workflows

4. AI Platform Engineering

Develop scalable AI services and applications using:

• Python

• FastAPI / Flask

• asynchronous processing

• distributed compute frameworks

Build production-grade APIs and AI services.

5. Enterprise AI Deployment & MLOps

Deploy AI models into scalable production environments.

Work with:

• Docker

• Kubernetes

• CI/CD pipelines

• MLflow / experiment tracking

• model monitoring and drift detection

Deploy AI solutions on:

• Azure

• AWS

• GCP

6. Data Integration & AI Systems

Work with enterprise data sources including:

• relational databases

• data warehouses (Snowflake, Redshift, BigQuery)

• data lakes (S3 / Azure Data Lake)

• vector databases (Pinecone, Weaviate, FAISS)

Required Skills:

Programming

Expert-level proficiency in:

• Python

• software engineering best practices

• data structures and algorithms

Experience building production-ready systems.

Machine Learning

Strong expertise in:

• supervised learning

• unsupervised learning

• deep learning

• time-series modelling

• model evaluation and optimization

Generative AI

Experience working with:

• LLM APIs

• prompt engineering

• RAG pipelines

• embeddings and vector search

AI Architecture

Ability to design:

• scalable AI systems

• distributed ML systems

• intelligent automation platforms

Preferred Experience

• Building enterprise AI products

• Developing AI copilots or agents

• Designing decision intelligence platforms

• Experience with large-scale data systems

Ideal Candidate Profile

The ideal candidate is:

• A strong ML engineer AND software engineer

• Comfortable building AI systems end-to-end

• Experienced in deploying models to production

• Passionate about next-generation AI architectures

We value builders who ship real systems, not just research prototypes.

Education

Bachelor’s / Master’s in:

Computer Science

Artificial Intelligence

Machine Learning

Data Science

or related field.

Why Join Ampera

At Ampera, we are building AI-native enterprise platforms that transform how organizations use data and intelligence.

Engineers at Ampera work on:

• real-world enterprise AI systems

• cutting-edge GenAI and agentic architectures

• global enterprise clients across industries

• high-impact AI platforms that scale.

What Makes This Role Unique

You will help build the next generation of enterprise AI systems — where AI moves beyond prediction and becomes an autonomous decision-making layer for organizations.

About Ampera:

Ampera Technologies, a purpose driven Digital IT Services with primary focus on supporting our client with their Data, AI / ML, Accessibility and other Digital IT needs. We also ensure that equal opportunities are provided to Persons with Disabilities Talent. Ampera Technologies has its Global Headquarters in Chicago, USA and its Global Delivery Center is based out of Chennai, India. We are actively expanding our Tech Delivery team in Chennai and across India. We offer exciting benefits for our teams, such as 1) Hybrid and Remote work options available, 2) Opportunity to work directly with our Global Enterprise Clients, 3) Opportunity to learn and implement evolving Technologies, 4) Comprehensive healthcare, and 5) Conducive environment for Persons with Disability Talent meeting Physical and Digital Accessibility standards

Accessibility & Inclusion Statement

We are committed to creating an inclusive environment for all employees, including persons with disabilities. Reasonable accommodations will be provided upon request.

Equal Opportunity Employer (EOE) Statement

Ampera Technologies is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.

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Job Responsibilities

• Help develop an analytics platform that integrates insights from diverse data sources.
• Build, deploy, and test machine learning and classification models
• Train and retrain systems when necessary
• Design experiments, train and track performance of machine learning and AI models that meet specific business requirements
• ML and data labelling: Identify ways to gather and build training data with automated data labelling techniques and the creation of highly accurate training datasets.
• Automatic extraction of causal knowledge from diverse information sources such as databases, news, social media, videos and images etc.
• Develop customized machine learning solutions including data querying and knowledge extraction.
• Develop and implement approaches for extracting patterns and correlations from both internal and external data sources using time series machine learning models.
• Work in an Agile, collaborative environment, partnering with other scientists, engineers, consultants and database administrators of all backgrounds and disciplines to bring analytical rigor and statistical methods to the challenges of predicting behaviors.
• Distil insights from complex data, communicating findings to technical and non-technical audiences.
• Develop, improve, or expand in-house computational pipelines, algorithms, models, and services used in crop product development.
• Constantly document and communicate results of research on data mining, analysis, and modelling approaches.
• 5+ years of experience working with NLP and ML technologies.
• Proven experience as a Machine Learning Engineer or similar role.
• Experience with NLP/ML frameworks and libraries       
• Proficient with Python scripting language
• Background in machine learning frameworks like TensorFlow, PyTorch, Scikit Learn, etc.
• Demonstrated experience developing and executing machine learning, deep learning, data mining and classification models. Conversant with the latest in NLP and NLU models including transformer architectures and in creating explainable AI
• Ability to communicate the advantages and disadvantages of choosing specific models to various stakeholders
• Proficient with relational databases and SQL. Ability to write efficient queries and optimize the storage and retrieval of data within the database. Experience with creating and working on APIs, Serverless architectures and Containers.
• Creative, innovative, and strategic thinking; willingness to be bold and take risks on new ideas.

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Mandatory Skills: AI : Artificial Intelligence .

Experience: 8-10 Years .

Title: Quantitative Developer

Location : Mumbai

Candidates preferred with Master's

Who We Are

At Dolat Capital, we are a collective of traders, puzzle solvers, and tech enthusiasts passionate about decoding the intricacies of financial markets. From navigating volatile trading conditions with precision to continuously refining cutting-edge technologies and quantitative strategies, our work thrives at the intersection of finance and engineering.

We operate a robust, ultra-low latency infrastructure built for market-making and active trading across Equities, Futures, and Options—with some of the highest fill rates in the industry. If you're excited by technology, trading, and critical thinking, this is the place to evolve your skills into world class capabilities.

What You Will Do

This role offers a unique opportunity to work across both quantitative development and high frequency trading. You'll engineer trading systems, design and implement algorithmic strategies, and directly participate in live trading execution and strategy enhancement.

1. Quantitative Strategy & Trading Execution

• Design, implement, and optimize quantitative strategies for trading derivatives, index options, and ETFs
• Trade across options, equities, and futures, using proprietary HFT platforms
• Monitor and manage PnL performance, targeting Sharpe ratios of 6+
• Stay proactive in identifying market opportunities and inefficiencies in real-time HFT environments
• Analyze market behavior, particularly in APAC indices, to adjust models and positions dynamically

2. Trading Systems Development

• Build and enhance low-latency, high-throughput trading systems
• Develop tools to simulate trading strategies and access historical market data
• Design performance-optimized data structures and algorithms for fast execution
• Implement real-time risk management and performance tracking systems

3. Algorithmic and Quantitative Analysis

• Collaborate with researchers and traders to integrate strategies into live environments
• Use statistical methods and data-driven analysis to validate and refine models
• Work with large-scale HFT tick data using Python / C++

4. AI/ML Integration

• Develop and train AI/ML models for market prediction, signal detection, and strategy enhancement
• Analyze large datasets to detect patterns and alpha signals

5. System & Network Optimization

• Optimize distributed and concurrent systems for high-transaction throughput
• Enhance platform performance through network and systems programming
• Utilize deep knowledge of TCP/UDP and network protocols

6. Collaboration & Mentorship

• Collaborate cross-functionally with traders, engineers, and data scientists
• Represent Dolat in campus recruitment and industry events as a technical mentor

What We Are Looking For:

• Strong foundation in data structures, algorithms, and object-oriented programming (C++).
• Experience with AI/ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
• Hands-on experience in systems programming within a Linux environment.
• Proficient and Hands on programming using python/ C++
• Familiarity with distributed computing and high-concurrency systems.
• Knowledge of network programming, including TCP/UDP protocols.
• Strong analytical and problem-solving skills.

A passion for technology-driven solutions in the financial markets.

Key Responsibilities

· Advanced ML & Deep Learning: Design, develop, and deploy end-to-end Machine Learning models for Content Recommendation Engines, Churn Prediction, and Customer Lifetime Value (CLV).

· Generative AI Implementation: Prototype and integrate GenAI solutions (using LLMs like Gemini/GPT) for automated Metadata Tagging, Script Summarization, or AI-driven Chatbots for viewer engagement.

· Develop and maintain high-scale video processing pipelines using Python, OpenCV, and FFmpeg to automate scene detection, ad-break identification, and visual feature extraction for content enrichment

· Cloud Orchestration: Utilize GCP (Vertex AI, BigQuery, Dataflow) to build scalable data pipelines and manage the full ML lifecycle (MLOps).

· Business Intelligence & Storytelling: Create high-impact, automated dashboards in to track KPIs for data-driven decision making

· Cross-functional Collaboration: Work closely with Product, Design, Engineering, Content, and Marketing teams to translate "viewership data" into "strategic growth."

Preferred Qualifications

· Experience in Media/OTT: Prior experience working with large scale data from broadcast channels, videos, streaming platforms or digital ad-tech.

· Education: Master’s/Bachelor’s degree in a quantitative field (Computer Science, Statistics, Mathematics, or Data Science).

· Product Mindset: Ability to not just build a model, but to understand the business implications of the solution.

· Communication: Exceptional ability to explain "Neural Network outputs" to a "Creative Content Producer" in simple terms.

JOB DETAILS:

* Job Title: Principal Data Scientist

* Industry: Healthcare

* Salary: Best in Industry

* Experience: 6-10 years

* Location: Bengaluru

Preferred Skills: Generative AI, NLP & ASR, Transformer Models, Cloud Deployment, MLOps

Criteria:

1. Candidate must have 7+ years of experience in ML, Generative AI, NLP, ASR, and LLMs (preferably healthcare).
2. Candidate must have strong Python skills with hands-on experience in PyTorch/TensorFlow and transformer model fine-tuning.
3. Candidate must have experience deploying scalable AI solutions on AWS/Azure/GCP with MLOps, Docker, and Kubernetes.
4. Candidate must have hands-on experience with LangChain, OpenAI APIs, vector databases, and RAG architectures.
5. Candidate must have experience integrating AI with EHR/EMR systems, ensuring HIPAA/HL7/FHIR compliance, and leading AI initiatives.

Job Description

Principal Data Scientist

(Healthcare AI | ASR | LLM | NLP | Cloud | Agentic AI)

Job Details

• Designation: Principal Data Scientist (Healthcare AI, ASR, LLM, NLP, Cloud, Agentic AI)
• Location: Hebbal Ring Road, Bengaluru
• Work Mode: Work from Office
• Shift: Day Shift
• Reporting To: SVP
• Compensation: Best in the industry (for suitable candidates)

Educational Qualifications

• Ph.D. or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field
• Technical certifications in AI/ML, NLP, or Cloud Computing are an added advantage

Experience Required

• 7+ years of experience solving real-world problems using:
• Natural Language Processing (NLP)
• Automatic Speech Recognition (ASR)
• Large Language Models (LLMs)
• Machine Learning (ML)
• Preferably within the healthcare domain
• Experience in Agentic AI, cloud deployments, and fine-tuning transformer-based models is highly desirable

Role Overview

This position is part of company, a healthcare division of Focus Group specializing in medical coding and scribing.

We are building a suite of AI-powered, state-of-the-art web and mobile solutions designed to:

• Reduce administrative burden in EMR data entry
• Improve provider satisfaction and productivity
• Enhance quality of care and patient outcomes

Our solutions combine cutting-edge AI technologies with live scribing services to streamline clinical workflows and strengthen clinical decision-making.

The Principal Data Scientist will lead the design, development, and deployment of cognitive AI solutions, including advanced speech and text analytics for healthcare applications. The role demands deep expertise in generative AI, classical ML, deep learning, cloud deployments, and agentic AI frameworks.

Key Responsibilities

AI Strategy & Solution Development

• Define and develop AI-driven solutions for speech recognition, text processing, and conversational AI
• Research and implement transformer-based models (Whisper, LLaMA, GPT, T5, BERT, etc.) for speech-to-text, medical summarization, and clinical documentation
• Develop and integrate Agentic AI frameworks enabling multi-agent collaboration
• Design scalable, reusable, and production-ready AI frameworks for speech and text analytics

Model Development & Optimization

• Fine-tune, train, and optimize large-scale NLP and ASR models
• Develop and optimize ML algorithms for speech, text, and structured healthcare data
• Conduct rigorous testing and validation to ensure high clinical accuracy and performance
• Continuously evaluate and enhance model efficiency and reliability

Cloud & MLOps Implementation

• Architect and deploy AI models on AWS, Azure, or GCP
• Deploy and manage models using containerization, Kubernetes, and serverless architectures
• Design and implement robust MLOps strategies for lifecycle management

Integration & Compliance

• Ensure compliance with healthcare standards such as HIPAA, HL7, and FHIR
• Integrate AI systems with EHR/EMR platforms
• Implement ethical AI practices, regulatory compliance, and bias mitigation techniques

Collaboration & Leadership

• Work closely with business analysts, healthcare professionals, software engineers, and ML engineers
• Implement LangChain, OpenAI APIs, vector databases (Pinecone, FAISS, Weaviate), and RAG architectures
• Mentor and lead junior data scientists and engineers
• Contribute to AI research, publications, patents, and long-term AI strategy

Required Skills & Competencies

• Expertise in Machine Learning, Deep Learning, and Generative AI
• Strong Python programming skills
• Hands-on experience with PyTorch and TensorFlow
• Experience fine-tuning transformer-based LLMs (GPT, BERT, T5, LLaMA, etc.)
• Familiarity with ASR models (Whisper, Canary, wav2vec, DeepSpeech)
• Experience with text embeddings and vector databases
• Proficiency in cloud platforms (AWS, Azure, GCP)
• Experience with LangChain, OpenAI APIs, and RAG architectures
• Knowledge of agentic AI frameworks and reinforcement learning
• Familiarity with Docker, Kubernetes, and MLOps best practices
• Understanding of FHIR, HL7, HIPAA, and healthcare system integrations
• Strong communication, collaboration, and mentoring skills

Hi,

Greetings from Ampera!

we are looking for a Data Scientist with strong Python & Forecasting experience.

Title                               : Data Scientist – Python & Forecasting

Experience                   : 4 to 7 Yrs

Location                       : Chennai/Bengaluru

Type of hire                  : PWD and Non PWD

Employment Type     : Full Time

Notice Period             : Immediate Joiner

Working hours           : 09:00 a.m. to 06:00 p.m.

Workdays                   : Mon - Fri

Job Description:

We are looking for an experienced Data Scientist with strong expertise in Python programming and forecasting techniques. The ideal candidate should have hands-on experience building predictive and time-series forecasting models, working with large datasets, and deploying scalable solutions in production environments.

Key Responsibilities

• Develop and implement forecasting models (time-series and machine learning based).
• Perform exploratory data analysis (EDA), feature engineering, and model validation.
• Build, test, and optimize predictive models for business use cases such as demand forecasting, revenue prediction, trend analysis, etc.
• Design, train, validate, and optimize machine learning models for real-world business use cases.
• Apply appropriate ML algorithms based on business problems and data characteristics
• Write clean, modular, and production-ready Python code.
• Work extensively with Python Packages & libraries for data processing and modelling.
• Collaborate with Data Engineers and stakeholders to deploy models into production.
• Monitor model performance and improve accuracy through continuous tuning.
• Document methodologies, assumptions, and results clearly for business teams.

Technical Skills Required:

Programming

• Strong proficiency in Python
• Experience with Pandas, NumPy, Scikit-learn

Forecasting & Modelling

• Hands-on experience in Time Series Forecasting (ARIMA, SARIMA, Prophet, etc.)
• Experience with ML-based forecasting models (XGBoost, LightGBM, Random Forest, etc.)
• Understanding of seasonality, trend decomposition, and statistical modeling

Data & Deployment

• Experience handling structured and large datasets
• SQL proficiency
• Exposure to model deployment (API-based deployment preferred)
• Knowledge of MLOps concepts is an added advantage

Tools (Preferred)

• TensorFlow / PyTorch (optional)
• Airflow / MLflow
• Cloud platforms (AWS / Azure / GCP)

Educational Qualification

• Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Mathematics, or related field.

Key Competencies

• Strong analytical and problem-solving skills
• Ability to communicate insights to technical and non-technical stakeholders
• Experience working in agile or fast-paced environments

Accessibility & Inclusion Statement

We are committed to creating an inclusive environment for all employees, including persons with disabilities. Reasonable accommodations will be provided upon request.

Equal Opportunity Employer (EOE) Statement

Ampera Technologies is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.