Data Scientist / Machine Learning Engineer / Generative AI & Agentic Systems

Role Summary

As a Data Scientist / Machine Learning Engineer, you will be an integral part of our AI team, designing, developing, and deploying advanced machine learning, deep learning, generative AI, and agentic AI solutions across industrial and automotive use cases.

You will work on solutions ranging from computer vision, predictive maintenance, anomaly detection, optimization, and intelligent automation to Retrieval-Augmented Generation, multimodal AI, AI copilots, Model Context Protocol integrations, and autonomous or semi-autonomous agentic systems.

The role combines strong machine learning engineering fundamentals with practical experience in modern generative AI architectures, Azure OpenAI-based applications, RAG systems, tool-using agents, MCP-based enterprise data integration, and multi-agent orchestration frameworks. You will collaborate closely with product, engineering, manufacturing, R&D;, IT, and business teams to convert operational challenges into scalable AI products deployed in real-world environments.

Your contributions will directly support improved quality, productivity, engineering efficiency, operational resilience, and innovation across the automotive sector.

Key Responsibilities

Design, develop, evaluate, and deploy AI solutions, including traditional machine learning, deep learning, generative AI, and agentic AI systems.

Design and implement generative AI applications using Azure OpenAI Service and other foundation model platforms, including enterprise copilots, intelligent assistants, document-processing systems, knowledge assistants, engineering support tools, and automated workflow solutions.

Develop Retrieval-Augmented Generation solutions using vector databases, embedding models, Azure AI Search, semantic search, document processing pipelines, reranking, grounding, citation mechanisms, and evaluation frameworks.

Design and implement agentic AI systems capable of planning, reasoning, retrieving information, invoking tools and APIs, coordinating workflows, and interacting with business applications under defined governance and safety controls.

Develop Model Context Protocol based integrations to securely connect AI agents with enterprise systems, databases, APIs, file repositories, engineering data platforms, business applications, and operational tools.

Build MCP servers and connectors that expose structured and unstructured enterprise data to AI agents in a controlled, secure, and reusable way.

Design multi-agent architectures where specialised agents collaborate on tasks such as document analysis, engineering support, quality investigation, planning, data retrieval, cost analysis, process optimisation, and report generation.

Integrate large language models and multimodal foundation models into scalable applications using prompt engineering, structured outputs, function calling, tool use, MCP tools, memory management, orchestration frameworks, and guardrails.

Develop AI agents that can interact with enterprise tools such as SQL databases, PostgreSQL, Cosmos DB, Kusto, Databricks, SharePoint, document repositories, REST APIs, and internal business platforms through secure tool-calling and MCP-based interfaces.

Work with Azure AI Foundry,



Azure OpenAI Service, Azure AI Search, Azure Machine Learning, Azure Document Intelligence, Azure Functions, Azure Kubernetes Service, and related cloud-native services to build and deploy enterprise-grade AI applications.

Implement robust evaluation and monitoring frameworks for generative AI and agentic systems, including response quality, grounding, hallucination, latency, cost, safety, reliability, traceability, tool-call accuracy, agent workflow success rate, and user-feedback metrics.

Define and implement governance mechanisms for agentic AI systems, including access control, human-in-the-loop approval, audit logging, tool permissioning, data boundaries, escalation paths, and safe failure handling.

Collaborate with cross-functional teams to understand business needs, identify high-value AI use cases, define success metrics, and translate technical solutions into measurable business impact.

Work with cloud and platform teams to deploy AI solutions securely using cloud-native services, APIs, containers, CI/CD pipelines, data platforms, monitoring tools, and enterprise security standards.

Apply responsible AI principles, including data privacy, access control, explainability, traceability, model governance, bias awareness, safety guardrails, and human-in-the-loop controls.

Develop and maintain technical documentation covering data sources, system architecture, MCP server design, agent workflows, model design, evaluation methodology, deployment processes, operational procedures, and governance requirements.

Stay current with developments in machine learning, deep learning, foundation models, Azure OpenAI, agentic AI, MCP, multimodal systems, MLOps, robotics AI, and industrial AI, and apply relevant advances to practical business challenges.

Requirements

Master’s degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Robotics, Engineering, Mathematics, or a related field.

Minimum of five years of professional experience in machine learning engineering, data science, AI engineering, generative AI engineering, or a related technical role.

Proven experience developing and deploying machine learning or deep learning models for real-world applications.

Strong Python programming skills, including experience building maintainable, tested, production-oriented software. Knowledge of other programming languages, notably C++ or .NET, is a significant plus.

Strong knowledge of machine learning architectures, techniques, and evaluation methods, particularly in computer vision, deep learning, anomaly detection, forecasting, optimisation, and generative AI.

Experience with large language models, embedding models, transformer architectures, prompt engineering, fine-tuning, inference optimisation, and model evaluation.

Hands-on experience with Azure OpenAI Service or equivalent LLM platforms for building enterprise-grade generative AI applications.





Experience building RAG systems, including document ingestion, chunking, embedding generation, vector search, semantic search, reranking, grounding, citation generation, and retrieval evaluation.

Experience with Azure AI Search, vector databases, semantic search platforms, or enterprise search systems.

Experience designing or implementing agentic AI systems, including tool calling, API integration, workflow orchestration, planning, multi-agent collaboration, structured outputs, memory, guardrails, and human approval mechanisms.

Experience or robust familiarity with Model Context Protocol, including MCP server development, MCP tool integration, secure data access, and connecting agents to enterprise systems.

Familiarity with agentic AI frameworks such as Azure AI Foundry Agent Service, Microsoft Agent Framework, Semantic Kernel, LangChain, LangGraph, AutoGen, CrewAI, or equivalent orchestration frameworks.

Ability to design AI agents that interact with databases, APIs, documents, business systems, and internal tools using secure and auditable integration patterns.

Ability to analyse complex data, identify practical opportunities, and translate technical findings into actionable business recommendations.

Strong communication skills, with the ability to explain technical concepts clearly to technical and non-technical stakeholders.

Ability to work effectively in cross-functional and Agile environments.

Self-motivated, pragmatic, and comfortable operating in fast-moving technical environments.

Preferred Qualifications

Experience in the automotive industry, manufacturing, industrial automation, robotics, engineering, or another complex industrial environment.

Experience with multimodal AI systems combining text, images, video, sensor data, CAD data, engineering drawings, production data, or time-series data.

Experience with Azure AI Foundry, Azure OpenAI Service, Azure AI Search, Azure Machine Learning, Azure Document Intelligence, Azure Kubernetes Service, Azure Functions, Databricks, or equivalent cloud AI services.

Experience building Azure OpenAI-based copilots, enterprise knowledge assistants, RAG applications, AI workflow assistants, or agentic automation platforms.

Experience with Model Context Protocol architecture, MCP servers, MCP clients, MCP tools, and secure enterprise tool exposure for AI agents.

Experience with vector databases, graph databases, knowledge graphs, semantic search, or enterprise search platforms.

Experience with structured and unstructured data integration from sources such as SQL databases, PostgreSQL, Kusto, Cosmos DB, SharePoint, file repositories, APIs, and enterprise applications.

Experience with simulation, synthetic data generation, digital twins, robotics, reinforcement learning, or sim-to-real workflows.

Experience building AI applications that require traceability, compliance, access management, auditability, reliability, or high availability in industrial environments.

Experience implementing LLMOps, MLOps, DevOps, CI/CD, monitoring, observability, model evaluation, prompt evaluation, and cost optimization practices for AI systems.

Experience working with Agile delivery methods, product teams, and enterprise IT or cloud platform teams.

📌 Data Scientist (Pune)
🏢 Faurecia
📍 Pune