Walk-in | Agentic AI

Tata Consultancy Services; Chennai District; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Tata Consultancy Services in Chennai invites engineers to design and build agentic AI workflows using LLMs, including tool calls, memory, and orchestration.

You will work with LangChain-based frameworks, MCP servers, and RAG pipelines; contribute to observability, deployment at scale, and responsible AI guardrails.

Qualifications

• Proficiency in agentic AI concepts and lifecycle.
• Experience with LLM-driven workflows and tool calls.
• Hands-on with agent frameworks (LangChain or equivalent).
• Experience with MCP for tool integration.
• Experience building RAG pipelines and knowledge sources.
• Experience with monitoring and observability of agents.
• Cloud platform engineering experience (Kubernetes/OpenShift, CI/CD).
• Knowledge of Responsible AI and guardrails.
• Programming experience in Python, .NET or Java with ML libraries.

Responsibilities

• Design and implement agentic workflows using LLMs.
• Integrate tools and data into agent pipelines.
• Build and maintain RAG and grounding strategies.
• Monitor agent behavior, logging, and alerts.
• Deploy at scale using container platforms and CI/CD.
• Ensure security and responsible AI guardrails.

Skills

Agentic AI concepts

LLM-driven workflows

LangChain frameworks

MCP proficiency

RAG pipelines

Agent observability

Cloud/Kubernetes/CI-CD

Responsible AI

Programming: Python/.NET/Java

Tools

LangChain framework

MCP servers

Kubernetes

OpenShift

CI/CD tooling

Job description

Job description

• Strong understanding of agentic AI fundamentalsgoal definition, planning, reasoning, task decomposition, and autonomous action loops.
• Design and build agentic workflows using LLMs, including tool/function calling, state management, memory, and orchestration patterns (single-agent/multi-agent).
• Hands-on experience with agent frameworks and patterns (e.g., LangChain-based agent frameworks or equivalents), including routing, workflows, and agent coordination.
• Model Context Protocol (MCP) proficiency for integrating tools and contextual data into agent workflows (tool discovery/usage via MCP servers).
• Build Retrieval-Augmented Generation (RAG) pipelines and gr ounding strategies for enterprise knowledge sources (e.g., internal repos/wikis/ticketing/content stores) to improve agent reliability.
• Agent observability & monitoring (AgentOps): track agent behavior, tool calls, outcomes, and quality signals; implement alerts/traces and feedback loops for continuous improvement.
• Cloud and/or platform engineering exposure for deploying agentic systems at scale (containers/Kubernetes/OpenShift, CI/CD, performance tuning, and secure operations).
• Responsible AI & guardrails: design human-in-the-loop controls, safe tool access, and security-aware policies for agents operating with credentials, data access, and budgets.
• Proficiency in programming languages such as Python, .Net or Java, with experience in relevant libraries and frameworks (e.g., TensorFlow, PyTorch, Keras).