AI writes the code.
It still doesn’t understand your business.
Someone has to make it work in there. That someone is a person — and finding them is what we do.
What this is
We fill a technical position on your team with a professional working from another country.
Not a project handed over, not an agency you brief and wait on. A person joins your team and works in it — your standups, your repo, your priorities — while the employment itself sits with us in the country where they live. That split is the whole model, and it decides two things you probably came here to ask about: who carries the legal and payroll obligations, and who decides what gets built.
We are their legal employer.
Payroll, compliance and employment obligations sit with us, in the country where they live.
You direct the work.
Architecture, code review and product decisions stay with your engineering leaders. We don’t take the project — we fill the position.
Ways to fill the role
Three ways in. Pick the one that sounds like your team.
They are not three products — they are three different situations we keep meeting. One team already knows the role and the skill and just can’t find the person. Another wants the same work done by fewer people over time, with AI carrying the repetition. A third would rather pay less up front and tie part of the fee to the result. The engineers and the employment model are the same in all three. What changes is how you buy, and how the risk is shared — and you can start in one and move to another.
You know the skill — Staff augmentation
A named role, a named skill, a start date. We find the person, employ them where they live, and your team directs the work.
You want to do more with fewer — AI-augmented teams
A squad that starts at six and is designed to end at two, as the context turns into artefacts your team keeps.
You want part of the fee tied to results — Outcome-based engagement
You pay less up front. The rest is tied to the numbers we agreed, measured at the end of the period — so we carry part of the risk with you.
The profiles nobody can find right now
AI didn’t remove the need for engineers. It concentrated it.
The generic roles got easier to cover and harder to justify. These three went the other way: scarcer, more senior, and far more expensive to leave open. A vacancy in any of them stops a roadmap, not a ticket. They are what we specialise in.
The questions you’re already asking
Who legally employs them, and what does that put on me?
Legal employer →What risk stays with me, no matter who I hire through?
Risk and liability →How do I assess someone my team can’t interview in depth?
Technical assessment →How long does it actually take — and what does the wait cost?
Scarce profiles and time to fill →Ways to fill the role
Framework-equipped talent.
Our engineers don’t arrive empty-handed. They bring the engineering frameworks and playbooks of the group — how code gets reviewed, how access is granted, how data is handled, and what an engineer is allowed to point a model at.
That buys you speed: your team doesn’t spend the first two months rebuilding what already exists, and the first pull request looks like the tenth.
And it buys you the part nobody asks about until it’s too late. An engineer who already works inside a security and compliance discipline doesn’t improvise one on your systems. With AI in the toolchain that matters more than it used to: a model with access to your data is a door somebody has to check, and “we’ll figure that out later” is how it stays open. Our people arrive with the answer already written down — reviewed across 30+ years and 200+ companies, several of them in regulated industries.
Method comes with the person — it doesn’t replace them.
30+
years
7,000+
engineers hired
200+
companies
25
technical recruiters
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Read →Tell us which position you can’t fill.
We’ll tell you who can fill it, how long it takes, and what stays on your side.
