Role & responsibilities
• Robust 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., Lang Chain-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 (Agent Ops): 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, protected 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 field

Preferred candidate profile Experience 4 to 15 years

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🏢 Tata Consultancy Services
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