Hire LLM engineers who make models behave.
Deep specialists in the model layer: fine-tuning and adaptation, distillation and quantisation, and the serving setup that makes a model fast and affordable enough to run. The people you hire when calling an API is not enough and you need to change the model itself.
At a glance
Model specialists who fine-tune, quantise, and serve the model itself.
Review the work LLM Engineers can own, the skills required, and the systems they can help you build.
- Who this is for
- Engineering leaders who need an engineer to work within their existing team.
- Topics
- LoRA
- PEFT
- RLHF basics
- Distillation
- vLLM
- Quantisation
What they own.
- Fine-tune and adapt models on your data and your rules
- Constrain models to reliable structured outputs at scale
- Benchmark fine-tunes against base models so you ship the right one
- Reduce hallucination at the model layer with grounding and self-checks
- Distil and quantise models to cut cost without losing quality
- Benchmark Claude, GPT, and open models for your specific task
What you can ship with them.
Tools they reach for in production.
- Training
- LoRAPEFTRLHF basicsDistillation
- Serving
- vLLMQuantisationBatchingLiteLLMOpenRouter
- Eval
- Custom evalsLLM-as-judgeRegression suites
- Stack
- PythonPyTorchHFClaude / GPT
Seniority: Engineers who have fine-tuned and shipped models, not only consumed APIs.
Five stages.
The top 3% remain.
Every stage asks the same question: can they keep AI running once real customers are using it? Getting something started is the easy part, and it is not what we screen for.
400 applicants
3%of applicants reach
your shortlist
What removes them
We start with something they built
100% → 20%One real system, pressed hard. How much traffic did it take? What broke first? Who got the call when it did?
We break something and watch them fix it
20% → 9%A working system with a bug hidden inside it. Anyone can build a demo in a weekend. Fixing code you have never seen is the actual job.
How will you know it is working?
9% → 5%Before they write anything, they have to tell us how they would test it, and what they would do when it gets an answer wrong.
Make it fast without running up the bill
5% → 4%We give them a speed target and a budget, then ask them to explain the tradeoffs they made to hit both.
It is late and the AI got it wrong
4% → 3%What do you do first? How do you find out what happened, undo it, and explain it to the customer in plain words?
Often hired together.
Generative AI Engineers
All-rounders who ship a whole AI feature end to end, or anchor a pod of the specialists below.
Prompt & Evaluation Engineers
The people who turn AI quality into a number you can ship against.
MLOps & AI Infra Engineers
The infra people who keep AI fast, efficient, and observable in production.
Find the people to accelerate your roadmap.
You don’t need more resumes. You need proven AI engineers embedded in your workflow and ready to build from day one. Tell us what’s missing and we’ll line up a shortlist.