AI / ML Engineer

• Build production RAG and agent systems on Azure OpenAI, Anthropic and open models
• Own retrieval quality end to end: chunking, ranking, grounding and citation correctness
• Build the evaluation suites — a fixed question set with known-good answers, scored on every change
• Design guardrails against prompt injection, data leakage and confidently wrong answers
• Instrument cost and latency per request, and make the trade-offs visible to the client
• Say plainly when a problem is better solved with a query, a rule or a classifier than with an LLM

What we’re looking for

• Three or more years in ML or software engineering, with production LLM systems you can describe in detail
• Strong Python, and comfort with the async and streaming shape of LLM applications
• Practical grasp of embeddings, vector search and where retrieval actually breaks
• You have run an evaluation and changed your mind because of it

Nice to have

Not required. Apply if the section above fits.

• Fine-tuning or preference tuning in production
• Document intelligence over messy PDFs and scans
  • MLOps: model versioning, drift monitoring, feature stores