Role & responsibilities

Agentic AI Engineer

Preferred candidate profile
Solid understanding of agentic AI fundamentals (goal 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 grounding 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, secure 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).

📌 Artificial Intelligence Engineer Chennai
🏢 Tata Consultancy Services
📍 Chennai