LLM Solutions Architect

Design end-to-end agentic architectures — tool-use schemas, intent parsing, multi-step orchestration, and safety guardrails — engineered for long-term ownership by product engineering teams, not solo maintenance.

Define the multi-modal interface strategy across our product portfolio: how the same capability is exposed via UI, API, SDK, and agentic natural language — consistently and without duplication.

Design the horizontal LLM platform layer — shared RAG pipelines, prompt libraries, vector search infrastructure, and evaluation frameworks — that product engineering teams can build on and operate independently.

Prototype rapidly to validate AI product hypotheses before full engineering investment. Prototype acceptance by product teams is a primary success signal.

Ensure every system you architect comes with the observability, documentation, and engineering runbooks needed for a product squad to take ownership confidently.

Shape product strategy alongside Product leadership: actively influence what AI capabilities get prioritized, in what order, and with what trade-offs.

Select and govern LLM providers and deployment strategies per use case — balancing cost, latency, accuracy, and privacy requirements.

Drive alignment across Engineering, Product, and Design on what 'agent-ready' means for each product surface.

Mentor engineers on LLM integration patterns, agent evaluation, and production deployment practices — building the team's capability to own what you design.