AI Hiring
LLM / NLP Engineer for Production AI
Searching for engineers who have taken NLP and LLM systems to production — RAG, vector databases, prompt engineering as an engineering discipline — and separating them from the GPT-wrapper crowd.
Client
Companies building LLM-powered products (semi-anonymous track record)
Challenge
LLM engineering is the most inflated résumé category on the market: everyone “works with GPT.” The hard part is verifying who has dealt with retrieval quality, hallucination control, evaluation and inference cost in a system real users depend on.
Hiring Strategy
Our screening separates production evidence from demo experience: we probe evaluation pipelines, failure stories, latency and cost decisions — the things you can't rehearse if you haven't lived them.
Search Process
Structured technical interviews on real architectures: how retrieval was built and measured, how the vector store was chosen, what broke in production and what the candidate personally did about it.
Result
A repeatable evaluation methodology for LLM/NLP roles — the same one now published as the Devsmatcher Method — validated across multiple searches in this profile.
Technologies
- NLP
- LLM
- RAG
- Vector DB
- Prompt Engineering
Business Impact
Clients get engineers verified on production criteria — which is exactly the difference between an AI feature that ships and a demo that stalls.
Key Takeaways
- “Works with GPT” means nothing; “owned retrieval quality under real traffic” means everything.
- Evaluation experience is the single strongest production signal in LLM hiring.
- The third “why?” in an interview still separates rehearsed answers from lived experience.
Don't lose months and budget on the wrong AI hire.
A 30-minute breakdown of your challenge. No pitch. We'll show how we'd approach it ourselves — including the option not to hire yet.