What You Must Have Actually Done

Not just what you know. What you have shipped.
Deployed 2–3 agent-based systems in production - stateful, multi-step, real users
Used LangGraph for multi-agent orchestration with memory, tool routing, and state management
Built projects where AI (Claude Code, Codex, Cursor) wrote significant portions of the code
Implemented RAG pipelines end-to-end - chunking, embedding, retrieval, re-ranking, evaluation
Integrated agents with real enterprise APIs - not just OpenAI playground or sample data
Debugged a production agent failure - and fixed it without blaming the model
Can articulate when NOT to use agents - that is how we know you have built things

Bonus - Real Differentiators
Experience with Claude Code CLI in team settings (CLAUDE.md, shared context, multi-session flows)
Familiarity with LangSmith for agent tracing, evaluation pipelines,



and debugging at scale
Has shipped something using MCP (Model Context Protocol) or similar shared-context tooling
QA/testing mindset for agents - systematic evaluation of non-deterministic outputs
Background in IT services or consulting - managing client expectations while building
Experience with SLMs, fine-tuning, or on-device/edge agent deployment

What We Are Not Looking For
Someone who lists LLMs on a resume but has only called the API in a Jupyter notebook
AI enthusiasts whose hands-on experience is less than a year old
People who explain everything in terms of frameworks they have never deployed
Consultants who can only narrate what others have built

📌 Agentic Engineering Lead Pune
🏢 Zensar Technologies
📍 Pune