Agentic AI developer - F2F discussion - Chennai

Tata Consultancy Services; Chennai District; On-site; INR 2,000,000 - 3,500,000; Full time

Job summary

Tata Consultancy Services in Chennai is seeking an experienced Agentic AI developer to design and implement autonomous AI agents, defining goals, planning, and action loops.

You will build workflows with LLMs, integrate tools, manage memory and state, and ensure secure, responsible AI practices while deploying on cloud platforms.

Candidates with 4–15 years of relevant experience and hands-on expertise in LangChain, RAG pipelines, and containerized deployments are encouraged to apply.

Qualifications

• Hands-on experience building agentic AI workflows with LLMs and tools.
• Proficiency in orchestrating single-agent and multi-agent systems.
• Experience with MCP-based tool integration and data source access.
• Knowledge of RAG pipelines, vector stores, and semantic search.
• Experience deploying on cloud platforms with containers and CI/CD.

Skills

Agentic AI basics

LLM workflows

Memory management

State management

Single/ multi-agent orchestration

AgentOps Observability

Cloud deployment

Responsible AI

APIs & data source integration

Tools

LangChain

LangGraph

AutoGen

MCP

Vector databases

Semantic search

CI/CD

Kubernetes

Azure

AWS

GCP

Job description

Greetings from Tata Consultancy Services (TCS)

What we are looking for : Agentic AI developer

Interview Mode : F2F discussion

Experience : 4-15 Years

• Strong understanding of Agentic AI fundamentals including goal definition, planning, reasoning, task decomposition, and autonomous action loops.
• Design and build AI agent workflows using LLMs with tool/function calling, memory management, and state management.
• Experience in single-agent and multi-agent orchestration frameworks such as LangChain, LangGraph, AutoGen, or equivalent platforms.
• Hands-on expertise in Model Context Protocol (MCP) for integrating tools and enterprise data sources.
• Build and optimise Retrieval-Augmented Generation (RAG) pipelines using vector databases, semantic search, and enterprise knowledge repositories.
• Develop secure integrations with APIs, databases, business applications, and external tools to enable autonomous agent actions.
• Implement AgentOps/Observability capabilities including monitoring, logging, tracing, evaluation, and feedback loops.
• Deploy and manage agentic applications on cloud platforms such as Azure, AWS, or GCP, with exposure to Kubernetes, containers, and CI/CD practices.
• Ensure Responsible AI and Security Guardrails through human-in-the-loop controls, safe tool access, governance, and compliance practices.