Machine Learning Engineer / Generative AI & Agentic Systems (Pune)

Role & responsibilities I) Machine Learning Engineer / Generative AI & Agentic Systems - Masters or Bachelors degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Robotics, Engineering, Mathematics, or a related field. - Professional experience of 2 to 3 years 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, especially in Automotive industries - Solid 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 LLM, 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 strong 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. .