What you'll do
- Design end-to-end AI architectures that map to real business goals, not just technical possibility.
- Architect AI solutions across common enterprise patterns: copilots, RAG and document intelligence, conversational AI, workflow automation, and multi-agent systems.
- Design AI agents with tool calling, memory, orchestration, and human-in-the-loop controls - and know when not to make something autonomous.
- Build AI-ready data layers: vector stores, semantic search, knowledge graphs, metadata, and RAG pipelines grounded in enterprise knowledge.
- Integrate AI with systems of record (ERP, CRM, HRIS, ITSM, finance, collaboration tools) via APIs and event streams.
- Drive AI adoption across the SDLC - AI-assisted design, coding, testing, review, documentation, and CI/CD - and measure whether it's actually helping.
- Provide technical leadership: architecture standards, governance, mentoring, and client engagement.
What you'll bring
- 8+ years in solution or software architecture, including 3+ years delivering production AI solutions (not just prototypes).
- Hands-on experience with at least one major LLM platform: OpenAI, Anthropic Claude, Google Gemini, AWS Bedrock, or Azure OpenAI.
- Practical experience with at least one agentic framework — LangGraph, LangChain, LlamaIndex, Semantic Kernel, or OpenAI Agents SDK.
- Strong Python, plus solid experience on at least one major cloud (AWS, Azure, or GCP).
- Hands-on experience building RAG systems and working with vector databases.
- Experience integrating systems with APIs, SQL/NoSQL, and enterprise integration patterns.
- Excellent stakeholder communication — you can explain tradeoffs to executives and engineers alike.
Nice to have
- TypeScript/JavaScript for full-stack or agent-tooling work.
- Knowledge graphs, data lakes/lakehouses, and metadata/semantic modeling.
- Daily use of AI developer tools (GitHub Copilot, Claude Code, Cursor) with a point of view on where they help.
- LLMOps/evaluation experience — prompt versioning, offline/online eval, guardrails, observability.
- Familiarity with AI governance, security, and compliance (data residency, PII handling, model risk).
- Prior consulting or client-facing delivery experience.