HOME/HIRE ENGINEERS

Hire Dedicated Engineers

Hire the specialist the work actually needs, and interview them yourself

AI/ML, full-stack and mobile engineers from our own in-house team, screened on production work rather than on a CV keyword match. You run the final interview and the decision is yours.

Book a free consultation See our work

A senior engineer replies within 24 hours — not a sales rep.

Why hiring for a specialism goes wrong

One job title, five different jobs

"Full-stack developer" describes someone who builds React screens against an existing API and also someone who designs the schema underneath it. "ML engineer" covers a researcher, a data plumber and a person who keeps inference costs down. If the brief does not say which one you mean, you will interview a stream of people who are perfectly good at the wrong thing.

The interview measures the wrong signal

Algorithm puzzles select for recent practice. Framework trivia selects for recent projects. Neither tells you whether someone can walk into a codebase with eight years of decisions in it and make a careful change. The candidate who interviews best is frequently not the candidate who ships best, and you find out in month three.

Ramp-up eats the quarter you were trying to save

Someone joins, then spends three weeks on environment setup, access requests and reading code with nobody assigned to answer questions. The capacity you bought arrives long after the date on the plan, and your senior people lose their own sprint to onboarding.

Marketplaces optimise for the placement, not the second year

A freelancer who has learned your billing rules and your deployment quirks is worth far more than an equivalent stranger. When the pool rotates, that value resets to zero and the invoice does not. Cheap per hour and expensive per outcome is the usual shape.

How we put an engineer in front of you

We are not a marketplace forwarding CVs. Every engineer we propose is someone on our own team whose code we have reviewed, so what we say about them is something we have to stand behind afterwards.

The role gets defined in engineering terms first

Before we shortlist, we agree with your tech lead what this person will own, which half of the stack they need real depth in, and what a good first month looks like. Ten minutes of that conversation removes most of the mismatch that would otherwise show up as three wasted interviews.

Vetted on shipped systems, not on a whiteboard

Our engineers are assessed on production work in the stack you need — what they built, what broke, what they changed afterwards. We tell you where each person is strong and where they are not, including the reservations, because a shortlist with no caveats in it is a sales document.

You interview, you decide, you can say no

Run your own technical interview with your own bar and your own questions. Reject anyone for any reason and we will go again. Nobody is assigned to your team without you having met them and agreed.

Seniority and tenure, rather than the lowest hourly number

Our engineers are in-house and salaried, which means we carry their development and we care whether they stay. Depth of experience and length of tenure on one client are the things we compete on. If your decision comes down to the cheapest rate on a marketplace, we will lose it and that is fine.

What this covers

Pick the piece you need, or bring us the problem and we'll tell you which applies.

Hire AI/ML Engineers

Most machine learning work is not modelling. It is data pipelines, evaluation sets and inference costs, and the engineer you want is the one who can tell you why a metric moved rather than which library produced it. Hire this role when you have a model or an LLM feature in production, or about to be, and nobody currently owns whether it is getting better or quietly worse.

Retrieval pipelines end to end: chunking strategy, embedding choice, and an evaluation set that catches when a change made answers worse
Serving and cost control — batching, caching, quantisation, and the judgement to use a smaller model where it is sufficient
Training and feature pipelines, labelling workflows, and the training/serving skew that erodes accuracy without any error being raised
Screening advice on distinguishing genuine ML engineers from data scientists who have only worked in notebooks

Hire Full-Stack Developers

A full-stack engineer earns their keep by taking a feature from schema change through API to interface without three handovers and two integration bugs. Hire this role when your backlog is full of small vertical slices and your bottleneck is coordination between specialists rather than raw capacity in either layer.

Vertical feature ownership: migration, endpoint, UI and tests shipped together as one reviewable change
Performance work where it actually hurts — the N+1 query, the missing index, the bundle that grew by a megabyte
Authentication, roles and multi-tenancy designed once rather than patched per feature
How to screen for real depth: strong full-stack engineers are deep on one side and genuinely competent on the other, not average across both

Hire Mobile Developers

Mobile is the one discipline where a bad release cannot simply be rolled back — it sits in a store queue while your users keep the broken build. Hire this role when you need someone who owns the whole release path, native or cross-platform, and who thinks about the mid-range Android device your users actually carry.

Native Swift and Kotlin, or React Native and Flutter when a shared codebase is the honest trade-off
Release engineering: store review rejections, staged rollouts, forced-update paths and crash triage after ship
Offline behaviour, background sync and sensible conflict resolution when the device reconnects
Cold start, memory and battery profiling on real low-end hardware rather than on a flagship simulator

The stack we build on

Chosen to fit the problem — not because it's what we used last time.

AI and machine learning

Python
PyTorch
TensorFlow
scikit-learn
LangChain
Hugging Face
MLflow

Full-stack web

TypeScript
React
Next.js
Node.js
Django
Laravel
PostgreSQL

Mobile

Swift
Kotlin
React Native
Flutter
Firebase
Fastlane

Shared engineering practice

Docker
Kubernetes
AWS
Terraform
GitHub Actions
Playwright
Datadog

How we'll work together

Every stage ends with something in your hands — not a status update.

01
Turn the vacancy into a skills profile
A working call with whoever the engineer will report to. We pin down the specialism, the depth required on each side of the stack, the overlap hours and the problems this person is expected to own within a quarter.
You get: A written role profile covering stack, seniority band, ownership and the questions we suggest you ask in interview.
02
Screen before we spend your time
We run the technical assessment ourselves against that profile — architecture reasoning, a code review exercise, and a conversation about a system they actually built. Only people we would put on our own hardest project reach your inbox.
You get: A short shortlist with real project history and our honest written notes on each candidate's strengths and gaps.
03
Your interview sets the bar
Interview as you would a permanent hire. Pair with them, review code together, ask about the thing that went badly. We stay out of the way apart from scheduling, because the decision has to be yours to be worth anything.
You get: Interview slots inside your working hours, plus a written summary of what was agreed about scope and start date.
04
Make the first weeks count
Access, environment and a codebase walkthrough are arranged before day one, with a deliberately small first ticket lined up. Merged code in the first week tells you more about fit than any further round of interviews would.
You get: An onboarding plan agreed with your lead, a named point of contact on our side, and early merged work you can review.

Where we've applied this

Healthcare

Engineers who have already worked inside access controls, audit logging and clinical data models, so patient-facing changes do not stall in review every sprint.

Logistics

People who have handled carrier APIs, tracking events and the awkward reality that two systems disagree about where a shipment is.

Retail and e-commerce

Mobile and frontend specialists for storefront, checkout and app work, where a slow page and a lost sale are the same event.

Finance

Backend engineers used to reconciliation, idempotent payment flows and code that a reviewer has to be able to trust line by line.

Education

Developers for learning platforms and assessment tooling, where enrolment peaks make load behaviour a product requirement rather than an afterthought.

Professional services

Full-stack hires for client portals, document workflows and reporting that internal teams rarely have the bandwidth to modernise.

Clients we've built for

Real products, in production, with real users on them.

evera

View project

OrbiSim

View project

Beat22

View project

Fit and Fab With Swati

View project

Questions buyers ask us

What drives what an engineer costs?

Seniority is the largest factor, then how scarce the specialism is, then how much of your working day you need covered. An ML engineer who can own a production inference path is scarcer than a competent React developer, and an engineer holding late evening hours for a US team is a different proposition to one on standard hours. We put the specifics in writing once the role profile is agreed, so you are comparing a real number rather than a range from a pricing page.

How quickly is someone actually productive?

Sooner than a permanent hire, mostly because we plan the first week instead of improvising it. Access, environment and a first small ticket are arranged before day one, and we aim for merged code inside the first week — not because that ticket matters, but because it exposes fit while it is still cheap to correct. Full productivity on a domain-heavy codebase still takes a few sprints, and anyone who tells you otherwise has not seen your code.

Do we interview candidates ourselves?

Yes, and we would push back if you did not want to. We screen first so you are not sifting, then you run whatever process you use for permanent hires — pairing, code review, system design, your call. You can reject anyone without justifying it, and nobody joins your team on our say-so alone.

How is this different from Toptal or a large offshore agency?

Marketplaces match you to a freelancer and step back; the engineer's incentives, tooling and career sit outside the arrangement. Our engineers are in-house and salaried, they are reviewed internally, and someone here is accountable for their growth and for whether they stay on your work. We are not the lowest number you will be quoted. We compete on the seniority of the person and on how long they stay after they have learned your system.

Will they work in our process and our timezone?

They join your board, your repository, your branching rules and your stand-up rather than running a parallel plan. Our team works from Punjab, India, which gives a natural afternoon overlap with the UK and Europe; for US clients engineers shift later so your morning is covered. We agree the specific window before anyone starts.

Who owns the code, and what about our security requirements?

Everything an engineer writes for you is yours, in your repository, from the first commit. They sign your NDA, work under your access controls, and can be restricted to company-managed devices or VPN-only access where your policy requires it. If you later want the work handed to an internal hire, we document and hand over rather than making that difficult.

Tell us which role you are trying to fill

Send the specialism, the stack and what this person would own in their first quarter. A senior engineer replies within 24 hours with candidates worth your time — and a straight answer if the role you have described is really two roles.

Book a free consultation Browse all services