GenAI Engineer
Summary
Builds and deploys production-grade multi-agent AI systems for enterprise clients, integrating with APIs, ERPs, and CRMs using frameworks like LangGraph and CrewAI.
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.
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
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