- · Strong 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 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, 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).

📌 Agentic AI Engineer (Chennai)
🏢 Tata Consultancy Services
📍 Chennai