Our new developers were able to hit the ground running, we've worked with them for over two years now, and they are truly part of our team.
Alison WhittleProduct Delivery Manager, BladeFSIn-house feel, nearshore
AI Engineers
The UK does not lack people who can call a model API. It lacks engineers who can take a model to production and keep it honest there. We embed AI engineers with real deployment experience, across ML, NLP and generative systems, into your team on British hours, for 50 to 75% less than the equivalent British hire would cost fully loaded.
- Model work carried through data pipelines, serving and monitoring
- Experience across LLMs, classical ML and applied inference
- Embedded in your team, your tools and your delivery cadence
PythonTensorFlowHuggingFaceLangChainFastAPI
Our Service
Vetted by CTOs on judgement, the scarce ingredient in AI work
We use AI in our own hiring, and the final decision is always human, which is exactly the posture we vet for in candidates. Every AI engineer passes technical assessment, live pair programming with a senior engineer, a cultural fit screen and our engineer-specific psychometric assessment, including how well they use AI tooling without outsourcing their thinking to it.
- Matched to your stack and the actual ML problem you have
- Fluent in both experimentation and hardened deployment
- Retention and delivery continuity backed by us as employer
End-to-End AI Capability
Engineers who connect the science to the shipped product
- Model DevelopmentTrain and tune models using Python, TensorFlow, PyTorch, HuggingFace, and more.
- Data EngineeringBuild reliable pipelines and prepare clean, usable datasets at scale
- ML OpsDeploy, monitor, and update models with reproducibility and performance in mind.
- GenAI & LLMsCustomize or fine-tune large models for your specific use case or ver
- Product IntegrationConnect models to real apps via APIs, microservices, or embedded logic.
- Experimentation & MetricsDesign and measure experiments that validate model and business performance.
Your AI Stack, Covered
Frontier model APIs, open weights and the pipelines between them
Technology coverage
From your front end to your back end, we've got your stack handled.
Frequently placed for AI Engineers
Python DevelopersBackend, data, and automation specialists
TensorFlow DevelopersML engineers that ship models
HuggingFace DevelopersNLP, transformers & model fine-tuning
LangChain DevelopersEmbedded LLM engineers
FastAPI DevelopersHigh-speed backend APIs
AWS DevelopersCloud-native engineers on demand
Vercel DevelopersNearshore Vercel experts
Rust DevelopersHigh-performance Rust expertise
Vetting
You see two people - not 200 CVs.
Engineers vet engineers
A senior engineer runs a live technical conversation on the candidate's real stack, not a recruiter working from a keyword list.
Live coding, not take-homes
Real problems solved in front of an assessor, with anti-cheating checks. You see how they actually work, not what they submitted overnight.
A full profile, not a CV
Every candidate arrives with a CV, interview summary, coding-test breakdown and rate in pounds. You decide on evidence.
Every tab is a stage a candidate has to clear. Six of them, all run by our own internal engineers at Cloud Employee, before you see a name.
- Overview
- CV
- Tech interview
- Coding test
- Psychometric
- Soft skills
Scored by people. We use AI to cross-check our own consistency - never to decide who reaches your shortlist.
✓Luka M.
VettedSenior AI Engineer · Python · LLM systems · RAG · 6 yrs · Zagreb, Croatia (GMT+1)
Assessment
Six years in production Python, the last three on LLM systems - retrieval pipelines, eval suites, and agent tooling for UK fintech. Owns delivery end to end.
Stack
Python · LangGraph
English
C1 fluent
Overlap
8 hrs UK
Notice
2 weeks
AI in practiceShips with Copilot and Claude Code daily, and rewrote 40% of the generated code in his observed task - the judgement is his, the tooling only makes him faster.
Career history
2023 - now
Senior AI Engineer
UK payments platform · 40-person product team
Built the retrieval assistant now resolving 30% of tier-1 tickets; owns its eval suite and guardrails.
2020 - 2023
Backend Engineer, Python
Travel booking SaaS · Zagreb
Moved fraud scoring onto a real-time feature pipeline; cut false declines by 18%.
2019 - 2020
Data Engineer
Agency · Python, Postgres, AWS
- Python
- FastAPI
- LangGraph
- pgvector
- Evals & tracing
- AWS
- Docker
Technical interview
Assessed by Marco R., Principal Engineer
12 Jun 2026
55 min · live call
"Walked me through an assistant that was quietly hallucinating refund policy, how he caught it in evals, and what he got wrong on the first fix. Hire-ready for a senior seat."
Live coding test
Repair a leaking retrieval pipeline
88
score
Tests passed
21 / 21
Time used
62 of 90 min
Anti-cheat flags
0
retrieval.py · submitted diff
- hits = index.query(q, k=50)+ hits = index.query(rewrite(q), k=50, filter=tenant)+ hits = rerank(hits, q)[:8] # recall 0.62 -> 0.91+ assert_context_budget(hits, max_tokens=6000)
Psychometric · technical thinking
How he reasons under a real deadline - not a personality quiz.
Top 9%of engineers
we test
A 45-minute reasoning test, scored against the 4,000+ engineers we have already placed. Our assessors do the marking - AI only flags where our own scoring looks inconsistent.
Soft skills · working with your team
Spoken English
C1 · assessed on call
References
2 of 2 verified
"He raised the payment edge case nobody else had spotted, in writing, two days before release."
This is what you receive - not a CV.
Ask our AI anythingWhy Cloud Employee?
A sane way to hire in an overheated market
AI salaries in London have detached from most budgets, and contractors in this field raise awkward IR35 questions. Our engineers are our full-time employees, so the compliance sits with us, and one monthly fee covers everything with no placement charge. Interviews within 7 working days, embedded in about two weeks, and a two-week money-back guarantee with a free replacement.
- Vetted for applied ML that survived contact with users
- Employer of record model, no contractor status ambiguity
- 97% of our engineers stay beyond two years
Not Just a Hire - A Long-Term Partner
Onboarding, HR and support handled, results left to the engineer
Fully supported
We handle the rest so your engineers can focus on building.
Performance reviews
Payroll & complianceEquipment
WorkspaceIn-house, without the friction
No upfront fees. No lock-in contracts.
- No placement or upfront fees
- Rolling 30-day contract, cancel anytime
- One monthly rate, everything included
Ready to find your engineer?
Tell us exactly who you need.
In 90 seconds
Two matched engineers in 7 days. No fees, no obligation.
Any particular stack?
Optional. Search anything, and add it if it is not listed.
Popular right now
- Founded in 2014
- 97% stay 2+ years
- Replace if it isn't working
Got questions?
The questions CTOs and founders ask.
01How much does it cost to hire an AI engineer in the UK?
Typically 50 to 75% less than a comparable British hire once salary, employer national insurance, pension, recruitment fees and overheads are counted. UK-based AI engineers are part of what we place, so that option is genuinely open. In practice most clients land in Eastern Europe, the Philippines or Latin America once they see the same calibre for materially less, and Eastern Europe fits a British working day almost exactly. We are offering the strongest quality-to-cost ratio we can find, exceptional engineers at a sustainable price, with geography the mechanism rather than the headline.
02What does an AI engineer do, and why do teams hire one?
An AI engineer builds production software around models, most commonly large language models; retrieval pipelines, evaluation harnesses, guardrails and the integration that turns a capable model into a dependable feature. Teams hire one when a prototype needs to become a product, because the gap between a convincing demo and a system that behaves acceptably for real users is the entire job. The role sits on a software engineering floor; without solid API design, data handling and testing instincts, the AI layer has nothing sound to stand on.
03What skills should I look for in an AI engineer now?
Evaluation thinking first, retrieval quality second, and a genuine software engineering floor underneath both; prompt tricks are the least durable skill on the list. A strong AI engineer starts by defining how output quality will be measured, builds the harness, and only then iterates, because without that loop every change is a guess. They understand why retrieval usually matters more than model choice, and reason about cost and latency as budgets rather than surprises. They treat prompts as versioned, tested artefacts.
04How do I assess an AI engineer, and what should I ask?
Give them a failure scenario, not a success story; describe an assistant that hallucinates confidently in production and ask them to walk you through the fix. Strong candidates go to measurement first, what the evaluation set looks like, how the failure was detected, what regression protects against its return. Weak ones go straight to swapping models. Ask how they would decide between improving retrieval and changing prompts, and what evidence would settle it.
05Are AI engineers in demand, and is the skill set worth building on?
Demand outstrips supply badly, and unlike several previous technology waves the work underneath is real; models are shipping inside products across every sector. The caution is about titles rather than the field. The label AI engineer is applied to everyone from research-adjacent specialists to developers who once called an API, so demand for the genuine article coexists with a flood of thin claims. Building on the skill set is worthwhile because the durable parts, evaluation discipline, data plumbing and integration judgement, transfer across model generations.
06Do your developers use AI tools, and how do you know the code is sound?
Yes, intensively, and in this role the question folds in on itself, since the job is partly to know exactly when AI output cannot be trusted. Our AI engineers use assistants to write harness code, generate test cases and explore approaches, and the soundness discipline is the same one they impose on the systems they build; nothing counts until an evaluation says so. During vetting we assess whether candidates use AI tooling well, and we also probe whether they can articulate its current failure modes.
07How does Cloud Employee vet AI engineers?
AI engineering candidates get the full CTO-led process, plus scrutiny recruiters cannot provide, because in this field the CV keyword and the actual capability sit furthest apart. The stages are a technical assessment, live pair programming with a senior engineer on realistic problems, a cultural fit screen and a psychometric assessment built specifically for software engineers. We look for production evidence, evaluation habits and the software fundamentals the AI layer stands on, and we test how candidates work with AI tools directly.
08How quickly can a developer start, and how does the engagement work?
You are typically interviewing matched candidates within 7 working days of a requirements call, and developers are often embedded and pushing code within about two weeks. From there it is a rolling monthly contract with 30 days' notice, one monthly fee, and no placement charges. You can scale up or down as the work changes, and you pay nothing to interview.
09What happens if the developer is not the right fit?
There is a two-week money-back guarantee, and if you want to continue we replace the developer free of charge. In practice this rarely comes up, because you interview a shortlist of two candidates who have already passed a CTO-led technical assessment and a live pair programming session, so the fit question is largely settled before anyone starts.
Proof, not promises
Hear from our customers
In their words
What the difference feels like
Alison WhittleProduct Delivery Manager, BladeFSWhat I love about Cloud Employee is that you have taken all of that hard work off my shoulders.
Marcus KilgourCTO, Salmon SoftwareWorking with Cloud Employee developers is great, because they're not afraid to push back. They're proactive, they're positive, and they feel like part of the team.
Doran EskinaziHead of Engineering, HealthpointeWe actually hired the whole team remotely, having never met them. And we made a bunch of really good hires.
Euan CameronCEO, WilloCloud Employee provides a level of professionalism, communication and support we haven't found elsewhere.
Trevor SathorManaging Director, Square EyeWorking in tandem with Cloud Employee we have successfully released a major version of our main software product during a critical period for us.
James StringerManaging Director, CleanLink