Job Overview
Role: Python Gen AI Engineer Location: Chennai Experience: Experienced Qualification: B.E / B.Tech / M.E / M.Tech Key Skills: Python, LangChain, LangGraph, LLM Fundamentals, Prompting, Tool Calling, RAG, Embeddings, Context Management, Memory Systems, API Design, Microservices, SDLC, CI/CD, Agent Evaluation, Observability, AI Governance, Security Guardrails, Access Control, Auditability, Human Oversight
Job Description
Cognizant is seeking experienced Python Gen AI Engineers in Chennai to build production-grade generative AI applications. This role requires strong Python development skills and hands-on experience with frameworks like LangChain and LangGraph for developing AI agents. Candidates must understand modern LLM concepts including prompting, tool/function calling, Retrieval-Augmented Generation (RAG), embeddings, context management, and memory systems. The position demands experience in designing reusable, modular, and platform-agnostic AI architectures. It combines generative AI engineering with traditional software engineering practices such as SDLC, CI/CD, version control, API design, and microservices. A key focus is AI reliability and governance, encompassing agent evaluation, observability, tracing, guardrails, access control, auditability, and human oversight. A B.E, B.Tech, M.E, or M.Tech qualification is required. Salary is "Best in Industry".
Skills and Eligibility Criteria
Educational Background: B.E, B.Tech, M.E, or M.Tech.
Experience: Relevant professional experience in software and generative AI engineering. Intended for experienced candidates, not entry-level.
Mandatory Technical Skills:
- Python (production-grade software, reusable modules, error handling, API integration, testing)
- LangChain and LangGraph (or similar agentic frameworks for LLM workflows)
- LLM Fundamentals (Prompting, Tool and Function Calling, RAG, Embeddings, Context Management, Memory Systems)
- API Design and Microservices
- SDLC and Software Engineering Skills (CI/CD, version control, code reviews, testing)
- Platform-Agnostic AI Architecture (modular, vendor-agnostic systems)
- Agent Evaluation and Observability
- AI Governance and Security Guardrails (Access Control, Auditability, Human Oversight)