Practical, specific advice on finding, screening and hiring production-ready engineers.
The hard part is representing a domain faithfully, which is older than any current tooling. Relational modellers and information-science backgrounds are routinely overlooked and frequently better.
Without this function every other improvement is a guess. An unusual profile, and the people who do it well often come from testing backgrounds rather than machine learning.
The constraint on your engineer is not the model, it is the environment. Describe the job honestly and you attract the right person; call it cutting-edge research and you attract somebody who wanted a research post.
A narrow, measurable job: make a fixed amount of GPU produce more answers per pound without the quality moving. What to ask, and the adjacent hires that are often better value.
The server exists to host many models from different frameworks behind one service. If you have one model in one framework you almost certainly do not need it, and a good candidate will say so.
You are not changing what the model computes, you are getting more throughput from the same NVIDIA hardware while proving the outputs did not shift. Measurable, specialist, and often fixed-length work.
Pipelines break because somebody else changed a column. The skill is negotiating a data contract and reprocessing six months safely, and it is chronically under-titled and under-paid.
A good CUDA engineer often begins by telling you the library implementation is already near the roof. That judgement is what you are paying for, and it is why the premium is real rather than hype.
Reframing this as a Kubernetes hire fixes most failed searches for the skill. What to test, who converts well, and the exit route to write down before you adopt it.
The software is not difficult; the value depends on discipline maintained by busy people under no obligation to comply. How to screen for somebody who has made that stick.
The only hire on this list where part of the value is controlling what you spend, because unlike an open-source stack the meter is running. What separates a competent user from a strong one.
Teams arrive for Gemini or for TPUs and the tooling comes with the territory. The hiring pool is a legitimate input to choosing a platform and is routinely left out of that decision.
Write down which three or four components your work touches before you write the advert. Endpoints bill for as long as they exist, which accounts for a remarkable share of unexpected invoices.
No UK employer advertises this title, and most should not hire it. Why reward specification is the real work, why simulators sink projects, and the three settings that justify a specialist.
Fraud is the commonest application and funds most of these roles. An engineer who wants to replace all the rules with a model has not yet explained a decision to a compliance officer.
An engineer who knows the classical result will save you a quarter. One who applies a neural network to eighteen months of weekly sales data will spend it.
A recommender changes the data it is trained on, and left alone the system narrows. How to screen for somebody who has met that, and the question to ask before you hire at all.
One employer in 1.1 million UK careers pages names NLP in a job title. The discipline was renamed, not retired, and the people who can still train a classifier from scratch are scarce.
A hiring guide for deep-learning vision: how much labelled data you need, who should label it, why a bigger model rarely fixes bad labels, and the deployment target that decides who is suitable.
An integration role rather than a modelling one. The best candidates frequently come from software engineering, because the discipline is making something work in an environment you cannot change.
You have a tabular prediction problem and this is very likely the right tool. Advertise the problem, not the library: a requirements list with the package name reaches people who have read the documentation.
The oldest technology on this site, and the people who know it best are often not ML engineers at all. A CV that says "machine vision" is usually fifteen years of exactly the right experience.
Churn, fraud, credit risk, forecasting and pricing are the models that run the actual economy, and they are systematically undervalued by candidates who find language models more interesting.
A candidate pool of JAX engineers barely exists at the size most employers want. How to decide whether you need the framework, and how to recruit into it if the choice is already made.
Three different hires hide under this description and only one is about the library. Why the teaching-background candidate is underrated, and when it should not be a requirement at all.
Ask what the model earns before you cost the rewrite. Pinned dependencies, an unowned runtime, and proving a retrained model still agrees with the old one.
Almost anyone can write a training loop, so the word on a CV means little. Where the API ends and the framework begins, three questions that find real depth, and why two runs of one script disagree.
Voice systems fail where the user hears it immediately and cannot scroll back. The strongest candidates often come from real-time audio or telephony rather than from machine learning.
The commercial problem is rarely making a nice image; it is making this image, on brand, twenty times. What that means for screening, for the contract, and for whose hardware pays.
Most multimodal products are two models and a join, and that is fine. What decides this hire is whether you own genuinely paired data, so audit that before you write the advert.
Agentic spans a two-tool chain and an autonomous long-horizon system, and the judgement needed differs at each end. Decide which you are buying before you advertise.
Ask anybody who shipped a document assistant where the time went and the answer is rarely the model. It is the PDFs. How to screen for that, and the realistic shape of a first three months.
You are hiring a backend engineer who happens to use the library, not a specialist in the library. The abstraction question, the upgrade question, and the four areas where trouble actually starts.
Retrieval systems are good in a fortnight and stuck for six months. How to screen for the diagnostic discipline that unsticks them, and what the hire costs in the UK.
Eight UK employers name this role in a title; 154 list it as a skill. A guide to modality, brand-safety enforcement, and judging output when quality is a matter of taste.
Most LLM engineer hires fail before the advert goes out, because nobody decided which of three roles it was. A practical guide to scoping, screening and interviewing for the one you need.
No UK employer advertises this title, yet the work is everywhere. Where training time and money actually go, why utilisation misleads, and what makes reserved capacity pay.
This role implements, scales and debugs ideas it did not originate. How to screen for that, where it differs from a research scientist, and why it is among the hardest hires in Britain.
Eleven UK employers put applied AI in a job title. The word is the entire specification: a result that reaches a product, which needs a route to production and somebody to walk it with.
A company with two models needs somebody to ship them; a company with forty needs somebody to build the road. The same advert attracts both, and the difference shows up in month five.
The deepest market on this site, so the difficulty is not supply but knowing which candidate you are looking at. Includes the one role we would not hire remotely.
The vaguest title in AI hiring, and how to make it specific: modality, train versus adapt, and the debugging story that sorts a decade of ability.
Anybody who has built something with a model API can claim this truthfully. How to sort a large pile fast without being unfair to the self-taught.
Both jobs are real and they want different people. Conflating them produces a shortlist where the strong candidates for each look weak against the other.