AI Architect

Job Description:

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

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 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
• 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