Applied AI Engineer

Nestium uses AI to accelerate real estate intelligence, not to invent it. You'll design and ship applied LLM systems: extraction from messy public sources, structuring into our data model, retrieval over market and geospatial context, and draft generation that a reviewer can trust. Eval, grounding and provenance are first-class — every AI output must be checkable before it reaches a client.

• Build production LLM pipelines for extraction, classification, summarisation and draft report sections — grounded in cited sources.
• Design retrieval and context assembly over structured market data, registries and geospatial layers (RAG done properly, not as a demo).
• Define evals, golden sets and regression checks so model or prompt changes don't silently degrade quality.
• Instrument cost, latency and failure modes; keep human review in the loop wherever the output informs a client decision.
• Work with data engineers and analysts so AI outputs land in the same provenance and confidence model as every other Nestium figure.

• You've shipped LLM or ML features to production — not just notebooks or prototypes.
• Strong Python; comfortable with APIs, data contracts and production debugging.
• You obsess over grounding, citation and failure cases; you don't trust a fluent answer without evidence.
• You can explain trade-offs (quality vs. cost vs. latency) to engineers and domain experts alike.