LLM engineers who've shipped to production.
Engineers who build retrieval, evaluation and guardrails into real products, not just prototypes. Each one interviewed by a practitioner on an LLM system they built.
What a strong hire does.
An LLM engineer turns large language models into reliable product features. The hard part isn't calling an API. It's making answers accurate, fast, affordable and safe once real users and real data arrive.
- Retrieval-augmented generation (RAG) over your documents and data
- Prompt and context design, structured outputs and tool calling
- Evaluation suites that catch regressions before users do
- Fine-tuning or distilling models when prompting isn't enough
- Cost, latency and caching trade-offs at production scale
- Guardrails for privacy, security and harmful output
Signs you need one now.
Your prototype works in demos but breaks on real data
You're adding AI features to an existing product
Nobody on the team owns LLM quality and evaluation
Interviewed by someone who has done the job.
- Python and modern LLM frameworks
- Vector search and retrieval design
- Evaluation methodology, not just demos
- Hosted versus open-weight model trade-offs
- Observability: tracing, logging and cost tracking
- Privacy and sensitive-data handling
Every candidate is also rated Strong, Solid or Developing on our six dimensions. Only those rated Strong on all six reach you. See the full process →
- "How did you measure answer quality, and what moved the number?"
- "Where did retrieval fail, and how did you fix it?"
- "What did each request cost, and what did you do about it?"
The right model for this role.
Contract or contract-to-hire
Most teams start on contract-to-hire: production experience now, and a clear path to bring the knowledge in-house.
30-day replacement
If a placement doesn't work out in the first 30 days, we replace them at no extra fee.
Free scoping call
30 minutes, then a written role brief within 48 hours. No fee until a candidate starts.
Questions we hear most.
What's the difference between an LLM engineer and an ML engineer?
An ML engineer typically trains and deploys models on your own data. An LLM engineer specializes in building products on large language models: retrieval, prompting, evaluation, tool use and guardrails. Many strong candidates do both, and the role brief from our scoping call clarifies which you need.
Can an LLM engineer work within our data and security rules?
Yes, and we test for it. Every candidate is screened on privacy, security and responsible AI, including how they've handled sensitive data in LLM systems.
How quickly can we see candidates?
After a free scoping call you get a written role brief within 48 hours, with a target shortlist date. We match from our vetted bench before searching further.
06 · Related roles
AI agent developers
Multi-step AI workflows with the right tools, limits and checkpoints.
Hire AI agent developers →ML engineers
Models taken from notebook to monitored production service.
Hire ML engineers →AI product managers
AI product managers and analysts who turn ideas into outcomes.
Hire AI product managers →Hiring LLM engineers? Let's scope it.
30 minutes, no cost, and you'll leave with a sharper role brief whether you hire through us or not.