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 - 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 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