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 Product Builds
Plenty of British firms have an AI pilot that impressed the board and never met a customer. We build the version that ships. Our engineers scope the use case, choose models on evidence rather than fashion, and deliver working software, all at 50 to 75% less than the equivalent London build team would cost once salaries and overheads are counted.
- LLMs and machine learning applied where they earn their place
- Scoped, engineered and shipped by one accountable team
- Shaped around your data, your users and your risk appetite
OpenAILangChainPythonTypeScriptPinecone
Our Service
From use case to production, with the hype filtered out
The hard part of an AI product is no longer the model, it is the judgement around it. What to automate, what to keep human, what the failure mode costs. Our engineers are assessed on exactly that judgement, through CTO-led vetting and live pair programming, and we will tell you plainly when a feature does not need AI at all.
- Scoping grounded in what users need, not what the model can do
- Full build, from model integration to the interface in front of it
- Infrastructure and data pipelines designed for real traffic
AI, Engineered for Delivery
Engineers who treat models as components, not magic
- Language Models (LLMs)Build AI-powered assistants, tools & chat UX using OpenAI, Claude, or open-source models.
- Machine Learning & RecommendationsImplement smart scoring, recommendations, or prediction systems using real data.
- Vector Search & EmbeddingsEnable fast, context-aware search using Pinecone, Weaviate, or similar tools.
- Data InfrastructurePrepare pipelines, ETL jobs, and secure storage to power your AI workflows.
- Full-Stack AI DeliveryWe build both the models and the interface - so users can use your AI today.
- Evaluation & MonitoringImplement versioning, scoring, and feedback loops to track model performance
Your AI Stack, Covered
Frontier APIs, open-weight models and the MLOps holding them together
Technology coverage
From your front end to your back end, we've got your stack handled.
Frequently placed for AI Product Builds
OpenAI DevelopersLLM-powered product specialists
LangChain DevelopersEmbedded LLM engineers
Python DevelopersBackend, data, and automation specialists
TypeScript DevelopersTyped safety meets modern JavaScript
Pinecone DevelopersVector search specialists
Vector DB DevelopersExperts in AI-native databases
HuggingFace DevelopersNLP, transformers & model fine-tuning
TensorFlow DevelopersML engineers that ship models
FastAPI DevelopersHigh-speed backend APIs
LlamaIndex DevelopersRAG app specialists
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?
Builders with a stake in what happens after launch
An AI build that ends at handover tends to decay quickly, because models drift and usage teaches you things the scoping could not. We stay on rolling monthly terms with 30 days notice, keep improving the product, and can embed the build team into your company full time when the product proves itself. A UK-based client success manager stays your point of contact throughout.
- Product decisions first, model choices second
- Built to be measured, iterated and defended to a board
- Convert the build team into embedded engineers when ready
More than Engineers
Talent, delivery and support teams around every AI build
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 build an AI product in the UK?
Expect to pay 50 to 75% less than an equivalent British build team when salary, employer national insurance, pension, recruitment fees and overheads are added up. Hiring a UK-based engineer through us is perfectly possible. It is simply that the quality-to-cost ratio usually points to Eastern Europe, the Philippines or Latin America, and for a British team Eastern Europe is the strongest of the three at about an hour ahead of London. The goal is the best quality-to-cost ratio available, exceptional builders at a sustainable price.
02What is an AI product build and why do teams choose one?
An AI product build is taking a model-powered idea to a shipped product, covering the data work, the model integration, the evaluation loop and the ordinary software around it. Teams choose a dedicated build when the AI is the product rather than a feature, because the failure modes are unusual; the hard part is rarely getting impressive output once, it is getting acceptable output reliably, at a cost per user that survives contact with a business model. A sound build ships a thin slice early, measures it against defined quality bars and grows from evidence.
03What skills should I look for in an AI product team now?
A blend, not a bench of one profile; you need evaluation-minded AI engineering, ordinary product engineering that keeps the ship watertight, and someone who owns cost and latency budgets. The failure pattern we see most is teams stacked with model enthusiasts and short on the boring competence, data pipelines, deployment and monitoring, that decides whether users ever see the clever part. Systems thinking outranks framework knowledge everywhere in the team, because AI has made producing code cheap while designing the loop, generation, measurement, correction, remains stubbornly human work.
04How do I assess an AI product team, and what should I ask?
Ask what they would ship in the first month, and distrust any answer that is not small; credible AI teams ship thin slices and measure, while the other kind promises platforms. Ask how they will know the product is good, and expect evaluation sets, quality thresholds and monitoring rather than adjectives. Ask what would make them recommend stopping, because a team with no stopping conditions will burn budget defending sunk cost. Probe the plumbing; where does your data live, what happens when the model provider has an outage, and how is a bad output caught.
05Are AI product builds in demand, and is it worth building one?
Demand is enormous, and a meaningful share of it should not exist; the field's uncomfortable secret is how many AI products are a feature wearing a product's clothing. Worth it turns on one question, whether the model's capability is the value or merely garnish. If your product dies when the AI layer is removed, a dedicated build makes sense, and moving early matters because evaluation data compounds. If the idea survives with the AI removed, build the product first and add intelligence where evidence demands it.
06Do your developers use AI tools, and how do you know the code is sound?
Yes, our build teams use AI assistants throughout, and on an AI product that is doubly true, because the product itself trains the team's instincts for machine output. Soundness rests on a rule that never bends; generated code is reviewed by the engineer who ships it, and generated behaviour is judged by evaluations, not impressions. In practice the assistant writes scaffolding, tests and pipeline plumbing at speed, while humans own architecture, data contracts and the quality loop.
07How does Cloud Employee vet engineers for AI product builds?
Every engineer on a build passes the same CTO-led vetting, a technical assessment, live pair programming with a senior engineer, a cultural fit screen and a psychometric assessment created for software engineers. For AI product work we weight the combination hardest, genuine software fundamentals underneath real AI-layer experience, because a build needs both and candidates strong in one often coast on the other. Pairing sessions surface it quickly, watching someone work a realistic problem, AI tools in hand, shows whether the checking loop is habit or performance.
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