Job Description

Responsibilities

Build Python services and APIs that expose document search, navigation and context retrieval to the AI assistant and agents, e.g. as tools or MCP servers.
Implement LLM and agent workflows: tool calling, multi-step orchestration, routing each question to document search, summaries or SQL over structured project data, streamed answers with citations.
Build the model gateway: model tiers, batch inference, caching, rate limits, retries, per-request token and cost budgets.
Integrate with the platform's existing application and PostgreSQL data layer, including user identity and document-level permissions enforced before results are returned.
Make it production-grade: automated tests including LLM-output evaluation in CI, tracing of latency and cost per question, security, infrastructure as code.
Agree interfaces, latency budgets and quality targets with the data/AI engineers; review code; keep