Key Responsibilities

Delivery & Architecture

- Own end-to-end delivery of AI-native programs - from architecture through production deployment
- Design and build multi-agent orchestration systems using LangChain, LangGraph, CrewAI, or equivalent
- Integrate agent systems with enterprise surfaces: APIs, ERPs, CRMs, data platforms - not toy datasets
- Define agent topology: tool routing, memory strategy, state machines, fallback handling

Agentic Coding & Development

- Run agentic coding workflows using Claude Code, Cursor, OpenAI Codex, or equivalent CLI tools
- Lead projects where AI writes significant portions of the codebase - and you guide, review, and ship it
- Work with CLAUDE.md, shared context frameworks, and multi-session agent setups for team use
- Debug non-deterministic agent outputs systematically - not by gut feel

Client & Stakeholder Engagement

- Translate business problems into agent architectures for global CXO-level stakeholders
- Run discovery workshops, solution reviews, and delivery cadences with client teams
- Prepare and present technical proposals, POC plans, and roadmaps - own the story end-to-end

Team & Practice

- Mentor junior AI engineers; raise AI engineering quality across the delivery team
- Stay current: evaluate recent models, frameworks, and tooling before the hype catches up
- Contribute to internal knowledge bases, reusable frameworks, and accelerators

Skills

Agent Orchestration

LangChain, LangGraph, CrewAI - not just conceptual

Agentic Coding Tools

Claude Code CLI, Cursor,



OpenAI Codex, Copilot

RAG & Vector Stores

Chroma, Weaviate, Pinecone - knows where RAG breaks

LLM APIs & SDKs

Anthropic, OpenAI, Gemini - prompt design, tool use

Python / TypeScript

Primary languages for agent + backend development

LangSmith / Observability

Tracing, evaluation, debugging agent runs

Cloud Platforms

Azure, AWS, GCP (at least one) - deployment, infra, managed services

API & System Integration

REST, gRPC, Kafka - enterprise integration patterns

MCP / Shared Context

Model Context Protocol, CLAUDE.md, Beads

Agent Evaluation

Testing non-deterministic outputs, guardrails, evals

CI/CD & DevOps

Git, containers, pipelines - agents need to ship

Client Communication

Can present architecture to a CXO without jargon

What You Must Have Actually Done

Not just what you know. What you have shipped.

- Deployed 23 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

📌 Agentic AI Lead (Pune)
🏢 Zensar
📍 Pune