Agentic Engineering Lead

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 environments (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 About the Role: This is not a slide-making or prompt-engineering role. We are looking for someone who has built multi-agent AI systems that run in production - not demos, not pilots that died after a sprint. You will anchor AI delivery programs end-to-end, work directly with global clients, and stay sharp on a field that changes every few weeks. You will report into and replicate the function of a senior AI delivery leader - which means you need both the depth to architect solutions and the presence to walk a CXO through what you built and why it works. 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 new 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 How We Will Evaluate You Not a theory round. Expect to walk through something you have actually built - architecture decisions, what broke in production, what you would do differently. If you cannot do that with specifics, this role is not the right fit. Evaluation stages: • Stage 1 - Technical screen: Walk us through a live agent system you built • Stage 2 - Architecture discussion: Given a business problem, design an agent solution on the spot • Stage 3 - Stakeholder simulation: Present your approach to a non-technical executive audience