Local LLM Engineer for FastAPI Backend

Python, NoSQL Couch & Mongo, Machine Learning (ML), Amazon Web Services, Node.js, AngularJS, API Development, FastAPI, AI Model Integration, AI Development · Local LLM engineer — structured JSON output behind a finished API (2–3 weeks, fixed scope) We have a finished demonstrator with one component left to build. FastAPI backend (~65 endpoints, 439 tests passing), React frontend complete, Docker Compose, runs offline on a laptop with 12 GB VRAM. One service currently runs as a deterministic rule engine behind a settled OpenAPI contract. We need a real local model in its place, generating up to three candidate recovery options as schema-valid JSON, in under 20 seconds. Retrieval is over a small static reference corpus — a few dozen pages. The interface is finished and deliberately permissive. Placeholders already exist wherever model output appears. No frontend work, no schema negotiation. The hard part: forcing schema conformance out of a mid-sized model degrades its reasoning. Valid JSON with weak content is worse for us than malformed JSON, because it fails silently. We are looking at LoRA adapters hot-swapped at inference to offset it. Tell us if you have a better answer. Scope note: this is a demonstrator. Everything except the generated options is deliberately deterministic and hard-coded, and the corpus is static stand-in data. We