Job Description

Role: Senior GenAI / AI-ML Engineer

Experience Range: 6+ Years

Location: Chennai

Mandatory Skillset: Langchain,LangGraph, Python,Fast API,RAG and Generative AI

Required Qualifications

We're looking for a Senior GenAI / AI-ML Engineer with strong hands-on experience in designing and deploying production-grade Generative AI solutions.

Must-have skills:

Bachelor's degree in Computer Science, Data Engineering, AI/ML, or a related field(Master's preferred).
5+ years of experience in AI/ML, with significant hands-on experience in Generative AI and LLM-based applications.
Strong hands-on experience with Generative AI / Large Language Models (LLMs), including model integration, evaluation, optimization, and deployment.
Strong expertise in Python and experience building scalable AI applications and services.
Hands-on experience with LangChain and LangGraph for developing LLM-powered applications, workflows, and AI agents.
Strong experience building Retrieval-Augmented Generation (RAG) solutions, including document ingestion, chunking, embeddings, retrieval, reranking, context management, and response generation.
Strong hands-on experience with FastAPI for developing and exposing AI/ML services and APIs.
Experience working with LLM APIs and model providers such as OpenAI, Azure OpenAI, Hugging Face, Anthropic, Gemini, or equivalent.
Good understanding of prompt engineering, embeddings, vector search, and LLM evaluation.
Experience integrating GenAI solutions with SQL/NoSQL databases, vector databases, and enterprise data sources.
Strong understanding of cloud platforms such as AWS, Azure, or GCP and their AI/ML services.
Understanding of MLOps, model lifecycle management, deployment, monitoring, and productionization of AI solutions.
Strong problem-solving, communication, and collaboration skills, with the ability to work with cross-functional teams.

Preferred Skills

Model Context Protocol (MCP) experience, including building or integrating MCP servers/tools with LLM applications or AI agents.
Experience developing AI Agents / Agentic AI workflows using LangGraph or similar frameworks.
Experience with multi-agent systems, tool calling, function calling, and structured outputs.
Experience with vector databases such as Pinecone, Weaviate, Qdrant, Chroma, Milvus, or FAISS.
Experience with LLM fine-tuning, LoRA/PEFT, or model optimization.
Experience with Docker, Kubernetes, CI/CD, and cloud-native deployments.
Strong understanding of AI security, data governance, responsible AI, and enterprise GenAI architecture.
Life Sciences / Pharma / Healthcare domain expertise is highly desirable.
Experience integrating GenAI solutions into production-level enterprise applications.