AES - DE - Generative AI Solution Architect

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

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