GenAI Engineer

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 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 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