How to hire AI engineers

Hiring AI engineers has settled into a consistent set of advice. Work out which engineer you actually need, because there are three: an applied or LLM engineer building with RAG and agents, a machine-learning engineer training and fine-tuning models, and an MLOps engineer running the infrastructure. Then, since everyone writes code with AI tools now, test judgment rather than syntax, whether they can reason about a system, catch where generated code is wrong, and design around latency and cost. The practitioners at Latent Space and in r/LLMDevs have that part well covered.

Where the advice goes quiet is the middle step: where you actually find these people. The usual answer is a marketplace, Upwork or a vetted network or an agency, because AI talent is scarce and hard to reach. That's one route. It isn't the only one, and it isn't the cheapest.

Define which AI engineer you mean

This first step earns its time, because "AI engineer" isn't one job. An applied engineer wiring an LLM into a product, a researcher fine-tuning a model, and an MLOps engineer keeping inference cheap and reliable are three different hires with three different skill sets. Naming which one you need, and the two or three tools that genuinely matter, does most of the filtering before you talk to anyone.

Where to find them is a pool, not a marketplace

Then the sourcing question, which is the one the guides route straight to a marketplace. The premise is that AI talent is too scarce to reach yourself. On cord that scarce talent is a searchable UK pool: more than 28,000 engineers list AI and machine-learning skills, from PyTorch and TensorFlow to LangChain and MLOps, and you message them directly. UK AI roles there advertise a median band of £60,000 to £90,000, so you can price the offer against real numbers, and a direct hire runs about £940 rather than a marketplace cut. You're reaching the same people the vetted networks would, without the fee to reach them.

Vet for judgment, not typing speed

Once you're interviewing, the practitioners are right and cord has nothing to add: drop the algorithm puzzles. Give a realistic task instead, a broken RAG pipeline to debug or a system to design under cost and latency constraints, and watch how the person reasons, where they trust an AI tool and where they check it. In a world where everyone can generate code, judgment is the thing worth testing, and it's yours to test.

So, where to actually look

The role definition and the interview are the parts the AI-hiring guides cover well. The gap they leave is the middle, and it's the expensive one: finding scarce specialists without paying a marketplace to introduce them. That's the step where reaching a direct pool changes the maths.

Want to see how many AI and ML engineers are already open to a move in your stack? That's a search on cord, before you brief anyone.

Hiring for a different stack? There's the same breakdown for Python, Java and React.