Ampera Technologies

About the Role

We are looking for a skilled SQL Server DBA with 4–5 years of experience in SQL Server database administration, performance tuning, and enterprise data integration. The ideal candidate should have hands-on experience working with Product Lifecycle Management (PLM) systems, ERP integrations, and data bridge solutions to enable seamless data exchange between enterprise applications.

Key Responsibilities

· Administer, monitor, and maintain Microsoft SQL Server databases to ensure high availability, security, and performance.

· Design, implement, and support PLM–ERP data bridge solutions for seamless integration between Product Lifecycle Management and ERP systems.

· Develop and optimize SQL queries, stored procedures, views, triggers, and database objects.

· Monitor database performance and perform query optimization, indexing, and troubleshooting.

· Design and implement database backup, recovery, disaster recovery, and high availability strategies.

· Build and maintain ETL processes and data synchronization workflows between PLM, ERP, and other enterprise applications. 

· Collaborate with application development teams to support database design and application deployments.

· Perform database migrations, upgrades, patching, and environment maintenance.

· Ensure database security, user management, and compliance with organizational standards.

· Create and maintain technical documentation, database architecture, and operational procedures. 

Required Skills & Experience

· 4–5 years of hands-on experience as a SQL Server DBA.

· Strong expertise in Microsoft SQL Server (2016/2019/2022 or later).

· Excellent knowledge of SQL, T-SQL, Stored Procedures, Functions, Triggers, Views, and Performance Tuning.

· Experience in database backup, restore, replication, indexing, and high availability (Always On, Log Shipping, Replication).

· Hands-on experience working with Product Lifecycle Management (PLM) systems.

· Experience implementing or supporting PLM–ERP data bridge/integration solutions.

· Knowledge of ERP systems such as SAP, Oracle E-Business Suite, Microsoft Dynamics, Infor, or similar platforms.

· Experience with ETL tools and enterprise data integration.

· Strong troubleshooting and root cause analysis skills. 

Preferred Skills

· Experience with Teamcenter, Windchill, Enovia, Arena PLM, or similar PLM platforms.

· Knowledge of SSIS, SSRS, and SSAS.

· Experience with PowerShell or Python scripting for database automation.

· Exposure to Azure SQL Database or cloud-based SQL environments. 

· Understanding of manufacturing, engineering, or product development processes.

· Familiarity with CI/CD and DevOps practices. 

Educational Qualification

· Bachelor's degree in Computer Science, Information Technology, Engineering, or a related discipline.

Key Competencies

· Strong analytical and problem-solving skills.

· Excellent communication and stakeholder management abilities.

· Ability to work independently in a remote environment.

· Strong attention to detail and commitment to database reliability and performance.

· Ability to manage multiple priorities in a fast-paced environment.

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. 

Role Overview

We are seeking a hands-on technology POD Lead who blends engineering excellence with statistical rigor and business acumen to drive end-to-end product delivery in an agile, data-driven environment. The ideal candidate will lead a multidisciplinary team of BI developers, data engineers, ML practitioners, and product analysts to accelerate business growth through scalable, AI-enabled products, econometric models and intelligent insights.

This role sits at the intersection of engineering, analytics, econometrics, MLOps and growth strategy, requiring a balance of technical depth, stakeholder engagement, and agile execution.

Key Responsibilities

1. Leadership & Delivery

• Lead a cross-functional pod of data engineers, BI developers, statisticians and machine learning engineers to deliver AI-powered products and analytics solutions.
• Translate strategic goals into data science roadmaps executed in agile sprints, ensuring measurable business outcomes for every release.
• Foster a culture of experimentation, accountability, and rapid iteration across data, AI, and product workstreams.

2. Product & Business Integration

• Partner with business stakeholders across Sales, Marketing, Finance, and Operations to identify high-impact use cases such as churn prediction, growth forecasting, pricing optimization, causal impact analysis or next-best-action recommendations.
• Drive the roadmap for analytical and econometric product capabilities (e.g., predictive dashboards, personalization engines, time-series forecasting, and more).
• Ensure all solutions are aligned with enterprise data strategy, governance, MLOps lifecycle and security standards.

3. Technical Execution

• Collaborate with ML engineers to productionalize models using Databricks, MLflow, Azure ML, or equivalent CI/CD MLOps frameworks.
• Guide teams on feature engineering, model selection, hyperparameter tuning, and validation for statistical and machine learning models.
• Encourage adoption of reusable data assets, API-based integrations, and modular code frameworks.
• Oversee econometric modeling, causal inference studies, and time-series forecasting for business-critical decision-making.
• Champion model lifecycle management, including version control, retraining pipelines, and performance drift monitoring.

4. Business Intelligence & Data Storytelling

• Supervise the creation of advanced BI dashboards and insight layers powered by predictive and generative AI.
• Translate complex statistical outputs into actionable business narratives for executive decision-making.
• Champion KPI alignment and measurement frameworks, ensuring analytics deliver quantifiable value to revenue, growth, retention, and operational efficiency metrics.

5. Agile Program Management

• Manage sprint planning, backlog prioritization, and resource allocation across concurrent projects.
• Track velocity, quality metrics, and ROI impact for each product stream.
• Coach teams on agile best practices and outcome-oriented delivery.

Qualifications

Required

• 10+ years of total experience with at least 3 years in a tech lead capacity.
• Proven expertise in Python, SQL, statistical modeling (e.g., regression, time-series, causal inference) and one or more of Power BI, Tableau, MicroStrategy.
• Strong foundation in data engineering, cloud architecture (Azure/AWS/GCP), and ML model deployment.
• Experience leading cross-functional agile teams with engineers, analysts, and data scientists.
• Excellent communication and stakeholder management skills — capable of simplifying complex data stories for business leaders.

Preferred

• Experience in forecasting models, econometrics, and experimental design (A/B testing, uplift modeling).
• Exposure to MLOps tools (MLflow, Kubeflow, Airflow, Azure ML pipelines) and monitoring frameworks for models in production.
• Familiarity with agentic AI, LLM-based product development, or generative analytics use cases.
• Prior experience building analytics or AI solutions in B2B, SaaS, or digital transformation contexts.
• Certifications in Agile, Cloud (Azure ML, AWS Data Analytics), or Data Science specialization are a plus.

Key Traits

• Hands-on technologist who can code, review, and guide with empathy.
• Strategic thinker who connects product vision with execution.
• Comfortable operating in ambiguity and scaling solutions from POC to enterprise rollout.
• Passionate about mentoring teams and embedding a data-first, growth-oriented mindset.

About the Role

We are looking for a Customer Success Analyst who can work closely with Agile/Scrum teams and bring customer insights into product development. This role will act as a bridge between customers, product, and engineering teams by leveraging data from customer success platforms like Planhat.

Key Responsibilities

• Collaborate with Scrum teams (Product Owner, Developers, Scrum Master) during sprint planning and reviews
• Bring customer insights, feedback, and product usage data into backlog prioritization
• Monitor customer health scores, adoption metrics, and churn risks using tools like Planhat or Gainsight
• Translate customer challenges into actionable user stories and requirements
• Track feature adoption post-release and provide feedback to product teams
• Work closely with Customer Success, Sales, and Support teams to ensure alignment
• Maintain and analyze customer data in CRM tools like Salesforce or HubSpot
• Support renewal and retention strategies by identifying at-risk accounts

Key Requirements

• 5+ years of experience in Customer Success / Business Analysis / Product Support / Account Management
• Basic understanding of Agile / Scrum methodologies
• Experience with Customer Success platforms (Planhat, Gainsight, or similar)
• Strong analytical and problem-solving skills
• Ability to interpret customer data and convert it into actionable insights
• Good communication skills to work with cross-functional teams
• Experience working in SaaS or product-based environments preferred

Good to Have

• Exposure to Agile tools (Jira, Confluence)
• Experience with product analytics tools (Mixpanel, Amplitude)
• Understanding of customer lifecycle management and SaaS metrics (churn, retention, LTV)
• Exposure to accessibility and inclusive product design

Role Overview

We are seeking an experienced Power BI & Microsoft Fabric Consultant to partner with our BI team for a short-term engagement focused on capability building, governance setup, and platform enablement.

The primary objective of this role is to upskill the team and establish a robust, scalable foundation for Power BI and Microsoft Fabric—covering security, governance, administration, and best practices—so that the team can independently manage and scale the platform post-engagement.

This is a hands-on + coaching role, not just advisory.

Key Responsibilities

1. Power BI & Fabric Enablement

• Conduct structured training sessions and workshops on:
• Power BI (Pro + Fabric-integrated experiences)
• Microsoft Fabric (Lakehouse, Warehouse, Dataflows Gen2, Pipelines, etc.)
• Build foundational understanding of:
• End-to-end analytics workflows in Fabric
• Integration between Power BI and Fabric workloads 
• Data Lake will be Snowflake

2. Governance & Security Framework

• Design and implement Power BI & Fabric governance model, including:
• Workspace strategy (Dev/Test/Prod separation)
• Naming conventions and standards
• Content lifecycle management
• Establish security architecture:
• Role-based access control (RBAC)
• Row-Level Security (RLS) / Object-Level Security (OLS)
• Data access patterns across Fabric and Power BI
• Define data sharing and access control processes

3. Administration & Platform Setup

• Configure and optimize:
• Power BI tenant settings
• Fabric capacity (capacity planning, workload management)
• Monitoring and usage metrics
• Set up:
• Deployment pipelines
• CI/CD best practices (where applicable)
• Audit logs and governance controls

4. Best Practices & Standards

• Define and document:
• Development standards (data modeling, DAX, report design)
• Performance optimization guidelines
• Dataset/reusable semantic model strategy
• Establish certification and promotion workflows for datasets and reports

5. Hands-On Implementation

• Work alongside the team to:
• Build or refactor key dashboards/reports using best practices
• Set up Fabric artifacts (Lakehouse/Warehouse/Pipelines)
• Ensure real use cases are implemented, not just theoretical training

6. Knowledge Transfer & Self-Sufficiency

• Provide:
• Playbooks, SOPs, and governance documentation
• Recorded sessions and training materials
• Mentor team members through:
• Office hours / Q&A sessions
• Code and architecture reviews
• Ensure the team can independently:
• Manage Fabric capacity
• Govern Power BI environment
• Implement secure and scalable solutions

Expected Outcomes (End of Engagement)

• Fully defined and implemented Power BI & Fabric governance framework
• Configured and optimized Fabric capacity + Power BI tenant
• Established security and access control processes
• Documented standards, playbooks, and operating model
• BI team capable of independent development, administration, and governance

Required Skills & Experience

Must-Have

• 5+ years of experience in Power BI development and administration
• Hands-on experience with Microsoft Fabric (end-to-end)
• Strong expertise in:
• Power BI governance and tenant administration
• Fabric capacity management
• Security models (RLS, RBAC, data access controls)
• Experience setting up enterprise BI governance frameworks
• Proven track record of training and mentoring teams

Good-to-Have

• Experience with data platforms (Snowflake, Azure, etc.)
• Knowledge of CI/CD for Power BI (DevOps integration)
• Familiarity with data catalog and lineage tools (e.g., Atlan, Alation)
• Understanding of modern data architecture patterns

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 

Job Description:

1.     Machine Learning Development & Deployment

·      Design and implement supervised and unsupervised models for predictive analytics, including churn prediction, demand forecasting, renewal risk scoring, and cross sell/upsell opportunity identification.

·      Translate business problems into ML frameworks and production solutions that improve efficiency, revenue, or customer experience.

·      Build, optimize, and maintain ML pipelines using tools such as MLflow, Airflow, or Kubeflow.

2.     Cross-Functional ML Use Cases

·      Partner with teams across Sales (e.g., lead scoring, next-best action), Customer Service (e.g., case deflection, sentiment analysis), Finance (e.g., revenue forecasting, fraud detection), Supply Chain (e.g., inventory optimization, ETA prediction), and Order Fulfillment (e.g., delivery risk modeling) to define impactful ML use cases.

·      Develop domain-specific models and continuously improve them using feedback loops and real-world performance data. 3.

3.      Model Governance and MLOps

·      Ensure robust model monitoring, versioning, and retraining strategies to keep models reliable in dynamic environments.

·      Work closely with DevOps and Data Engineering teams to automate deployment, CI/CD workflows, and cloud-native ML infrastructure (AWS/GCP/Azure).

4. Data Engineering and Feature Architecture

·      Collaborate with data engineers to define feature stores, data quality checks, and model-ready datasets on platforms like Snowflake or Databricks.

·      Perform feature selection, transformation, and engineering aligned with each domain’s business logic. 5. Communication & Stakeholder Collaboration

·      Present technical insights and model results to business and executive stakeholders in a clear, actionable format.

·      Work with Product Owners and Program Managers to scope, prioritize, and plan delivery of ML projects.

Qualifications:

Required

• Bachelor’s or Master’s degree in (e.g., Computer Science, Engineering, Statistics, Mathematics)

• 4+ years of experience in machine learning, data science.

• Proficiency in Python, XGBoost, PyTorch, TensorFlow, or similar.

• Experience deploying models into production using ML pipelines and orchestration frameworks.

• Strong understanding of data structures, SQL, and cloud platforms (e.g., AWS SageMaker, Azure ML, or GCP Vertex AI).

• Hands-on experience in implementing machine learning algorithms such as Random Forest, XGBoost, Logistic Regression, and Deep Learning techniques including Neural Networks (ANN, CNN)

Preferred:

• Experience supporting business functions such as Finance, Sales, or Operations with ML use cases.

• Familiarity with MLOps tools (MLflow, SageMaker Pipelines, Feature Store).

• Exposure to enterprise data platforms (e.g., Snowflake, Oracle Fusion, Salesforce).

• Background in statistics, forecasting, optimization, or recommendation systems.

Hi ,

We are looking for Oracle Incentive Compensation & Order Management Techno-Functional Consultant

PFB the Job Description:

Job Title: Oracle Incentive Compensation & Order Management Techno-Functional Consultant

Experience: 5+ Years

Location: Remote

Key Responsibilities

Oracle EBS & Incentive Compensation

• Design, configure, and implement Oracle Incentive Compensation (OIC) solutions within Oracle EBS R12.
• Analyze and integrate sales compensation plans, quota management, and commission calculations.
• Configure Plan Elements, Rate Tables, Compensation Plans, Pay Groups, and Sales Rep structures.
• Develop and enhance commission and incentive reporting frameworks.

Technical Development

• Develop PL/SQL packages, procedures, and functions for enterprise applications.
• Build RICEW components (Reports, Interfaces, Conversions, Extensions, Workflows).
• Create XML Publisher (BI Publisher) reports using RTF and XSLT templates.
• Develop and enhance custom workflows and concurrent programs.

Integration & Cloud Technologies

• Design and implement integration solutions using Oracle Integration Cloud (OIC).
• Develop REST web services using Oracle EBS Integrated SOA Gateway (ISG).
• Build integrations between Oracle EBS and external applications (Salesforce, ERP Cloud, third-party systems).
• Implement inbound and outbound interfaces for enterprise data exchange.

Implementation & Support

• Participate in full lifecycle implementations, upgrades, and system stabilization projects.
• Conduct requirements gathering, PRD preparation, and functional/technical design documentation.
• Perform unit testing, integration testing, and UAT support.
• Provide production support and issue resolution for business-critical applications.

Data Migration & Bulk Data Handling

• Use SQL*Loader, Export/Import utilities, and data loaders (FBDI, HCM DL) for large-scale data migration.
• Manage data conversion from legacy systems to Oracle EBS/Fusion applications.

Technical Skills

ERP & Cloud Platforms

• Oracle E-Business Suite R12
• Oracle Incentive Compensation (OIC / ICM)
• Oracle Fusion ERP Cloud (Finance & SCM)
• Oracle Integration Cloud (OIC)

Development Technologies

• PL/SQL
• SQL
• XML / XSLT
• REST Web Services

Tools & Utilities

• TOAD
• PL/SQL Developer
• SQL*Loader
• Oracle Forms 6i
• Oracle Reports 6i
• BI/XML Publisher

Databases

• Oracle 9i / 10g

Functional Knowledge

• CRM Foundation
• Core HR
• P2P, O2C cycles
• Order Management, Inventory, Purchasing

Job Description:

We are seeking a Cloud & AI Platform Engineer to design and operate AI-native infrastructure that supports large-scale machine learning, generative AI, and agentic AI systems. 

This role will focus on building secure, scalable, and automated multi-cloud platforms across AWS, Azure, GCP, and hybrid on-prem environments, enabling teams to deploy LLMs, AI agents, and data-driven applications reliably in production. 

You will work at the intersection of cloud engineering, MLOps, LLMOps, DevOps, and data infrastructure, helping build platforms that support RAG pipelines, vector search, AI model lifecycle management, and AI observability. 

Key Responsibilities

AI & Agentic Infrastructure 

• Design infrastructure to support agentic AI systems, autonomous agents, and multi-agent workflows. 
• Build scalable runtime environments for LLM orchestration frameworks. 
• Enable deployment of AI copilots, assistants, and autonomous decision systems. 

Common frameworks may include: 

• LangChain 
• LlamaIndex 
• AutoGPT 

LLMOps & AI Model Lifecycle 

Design and manage LLMOps pipelines for the full lifecycle of large language models: 

• Model deployment 
• Prompt management 
• Versioning 
• Evaluation and testing 
• Model monitoring 

Integrate with AI platforms such as: 

• Azure Machine Learning 
• Amazon SageMaker 
• Vertex AI 

Retrieval-Augmented Generation (RAG) Infrastructure 

Design and optimize RAG pipelines that integrate enterprise knowledge with LLMs. 

Responsibilities include: 

• Document ingestion pipelines 
• Embedding generation workflows 
• Knowledge indexing 
• Query orchestration 
• Retrieval optimization 
• Support scalable semantic search architectures. 

Vector Database & Knowledge Infrastructure 

Deploy and manage vector databases used for AI applications and semantic retrieval. 

Common technologies include: 

• Pinecone 
• Weaviate 
• Milvus 
• FAISS 

Responsibilities include: 

• Index optimization 
• Query latency tuning 
• Scalable embedding storage 
• Hybrid search architecture 

Multi-Cloud AI Infrastructure 

Design and maintain AI-ready infrastructure across: 

• Amazon Web Services 
• Microsoft Azure 
• Google Cloud Platform 

Key responsibilities include: 

• GPU infrastructure management 
• Distributed training environments 
• Hybrid cloud integrations with on-prem data centers 
• Infrastructure scaling for AI workloads 

Data Platforms & Integration 

• Support deployment and optimization of data lakes, data warehouses, and streaming platforms. 
• Work with data engineering teams to ensure secure and scalable data infrastructure. 

Cloud Architecture & Infrastructure 

• Design and implement scalable multi-cloud infrastructure across Azure, AWS, and Google Cloud. 
• Build hybrid cloud architectures integrating on-premise environments with cloud platforms. 
• Implement high availability, disaster recovery, and auto-scaling architectures for AI workloads. 

DevOps, Platform Engineering & Automation 

Build automated cloud infrastructure using modern DevOps practices. 

Tools may include: 

• Terraform 
• Docker 
• Kubernetes 
• GitHub Actions 

Responsibilities include: 

• Infrastructure as Code (IaC) 
• Automated deployments 
• CI/CD pipelines for AI models and services 
• Platform reliability and scalability 

AI Observability & Monitoring 

Implement observability frameworks to monitor AI systems in production. 

This includes: 

• Model performance monitoring 
• Prompt evaluation 
• Hallucination detection 
• Latency and throughput analysis 
• Cost monitoring for LLM usage 

Tools may include: 

• Arize AI 
• WhyLabs 
• Weights & Biases 

Security, Governance & Responsible AI 

Ensure AI systems follow strong governance and security practices. 

Responsibilities include: 

• Data privacy and compliance 
• Model governance frameworks 
• Secure model deployment 
• Monitoring model bias and drift 
• AI risk management 

Support enterprise frameworks for Responsible AI and AI compliance. 

Data & Security 

• Experience with data lake architectures, distributed storage, and ETL pipelines 
• Knowledge of data security, encryption, IAM, and compliance frameworks 
• Familiarity with AI governance and responsible AI practices 

Required Skills 

Cloud & Infrastructure 

• Strong experience in Azure (must have), AWS or GCP 
• Hybrid and multi-cloud architecture 
• GPU infrastructure management 

DevOps & Automation 

• Kubernetes 
• Docker 
• Terraform 
• CI/CD pipelines 

AI / ML Platforms 

• MLOps pipelines 
• Model deployment 
• Model monitoring 

AI Application Infrastructure 

• Vector databases 
• RAG pipelines 
• LLM orchestration frameworks 

Programming 

Experience in one or more languages: 

• Python 
• Go 
• Java 
• TypeScript 

Preferred Qualifications 

• Experience building AI copilots or autonomous agents 
• Knowledge of distributed model training - Knowledge of GPU infrastructure and distributed training 
• Familiarity with AI evaluation frameworks - Familiarity with model monitoring, drift detection, and AI observability 
• Experience building enterprise AI platforms 

Education & Experience 

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field 
• 4–8+ years experience in cloud infrastructure, DevOps, or platform engineering 
• Experience working in data-driven or AI-focused environments 

What Success Looks Like 

• Reliable ML model deployment pipelines - Reliable infrastructure for LLMs and AI agents, Scalable RAG knowledge platforms 
• Efficient multi-cloud infrastructure management - Fast deployment cycles for AI products 
• Secure and scalable AI-ready cloud platforms 
• Strong automation and governance across cloud and AI systems 

Job Description:

We are looking for a skilled Ethical Hacker (Penetration Tester) who will be responsible for identifying vulnerabilities in systems, networks, and applications before malicious hackers can exploit them. The role involves conducting security assessments, penetration testing, and recommending security improvements to strengthen the organization’s cybersecurity posture.

Key Responsibilities

·      Conduct penetration testing on web applications, mobile applications, APIs, and networks.

·      Identify security vulnerabilities and weaknesses in systems and infrastructure.

·      Perform vulnerability assessments using automated tools and manual techniques.

·      Simulate cyberattacks to evaluate the effectiveness of existing security measures.

·      Prepare detailed security reports highlighting risks, vulnerabilities, and remediation strategies.

·      Collaborate with development, DevOps, and IT teams to fix security gaps.

·      Ensure compliance with security standards and frameworks such as OWASP, ISO 27001, and NIST.

·      Conduct security audits and risk assessments across digital platforms.

·      Stay updated on the latest hacking techniques, security vulnerabilities, and cyber threats.

Required Skills & Qualifications

• Bachelor’s degree in Computer Science, Cybersecurity, Information Technology, or related field.
• 4+ years of experience in ethical hacking, penetration testing, or cybersecurity.
• Strong knowledge of network security, system security, and application security.
• Experience with security tools such as:
• Burp Suite
• Metasploit
• Nmap
• Wireshark
• Kali Linux
• Knowledge of OWASP Top 10 vulnerabilities.
• Understanding of Linux, Windows, and cloud security environments.
• Strong analytical and problem-solving skills.

Preferred Certifications

• CEH (Certified Ethical Hacker)
• OSCP (Offensive Security Certified Professional)
• CompTIA Security+
• CISSP (optional but valuable)

Key Competencies

• Cybersecurity risk assessment
• Vulnerability management
• Penetration testing methodologies
• Incident response awareness
• Strong documentation and reporting skills

Nice to Have

• Experience in cloud security (AWS, Azure, GCP)