Trusted by ambitious teams to embed senior AI engineers.
What we doWhat is a forward-deployed engineer - and who provides them?
A forward-deployed engineer is a senior engineer who works embedded inside your team - in your standups, your repositories and your production environment - rather than delivering from a vendor's office. The model started at Palantir; every major AI lab now runs a version of it. &above, an embedded AI build partner in London, provides forward-deployed engineers as a service across the UK: we hire and manage the engineers, and they build with your team.
Building AI in-house is the right instinct. The system ends up owned by your team, and the knowledge compounds inside your organisation instead of a vendor's. The hidden cost is the burden that comes with it: hiring senior AI engineers is slow and risky, managing them pulls your staff away from the day job, and internal builds too often stall unmaintained once the first workflow ships. Forward-deployed engineers remove that burden without giving up the ownership. We carry the hiring and the management; the engineers work embedded in your team as if they were your own; and the capability transfers to your people as the work ships.
We deliver in two tiers. Forward-deployed engineers are the first: senior engineers embedded in your team against a scoped workflow. AI Product Squads add product management and design in the same embedded team, for when the harder question is what to build rather than how - see our full AI consultancy & product development services. And because &above does not sell a platform, the architecture is never optimised for one model provider - we build automation, agents and AI software on whatever stack fits your constraints.
In-house delivery, without the in-house burden
What forward-deployed engineers change - senior engineers inside your team from week one, none of the hiring risk, and capability that stays when the engagement ends.
Embedded in your team
Your standups. Your repos.
Forward-deployed engineers work inside your team the way permanent hires do - writing code in your repositories, shipping against your real data, and accountable for the workflow in production rather than a document at the end.
Our embedded squad of 10 worked inside Tesco for 2.5 years, cutting creative compliance reviews from four weeks to days.
Hired & managed by &above
No hiring risk. No management load.
We recruit, vet and manage the engineers, so senior AI capability lands in your team in weeks - without the recruitment timeline, the management overhead, or staff pulled from the day job to run an internal build.
We built AI products used daily by 5,000 Google Cloud sellers.
Capability that transfers
Owned by your team, for good.
The engineers build alongside your people, so the capability transfers as the work ships - your team owns the system and can extend it without us. When the question becomes what to build next, the engagement expands into an AI Product Squad with product management and design.
We designed 80+ AI Innovation Lab engagements for Google's top customers.
Our work
Forward-deployed engineering for Google Cloud, Tesco, and other ambitious teams.
Forward-deployed engineer FAQs
Straight answers for teams weighing forward-deployed engineers against hiring their own.
- What does a forward-deployed engineer do?
- A forward-deployed engineer scopes, builds and ships a workflow from inside your team - attending your standups, writing code in your repositories, and staying accountable for the system in production. The role owns a problem end to end rather than picking up assigned tickets, which is what separates it from a traditional contractor.
- Where does the forward-deployed engineer model come from?
- Palantir built the model in the early 2010s around a simple idea: put the person who understands the problem and the person who ships the fix in the same seat, so nothing is lost in a handoff. Every major AI lab now runs a version of it, and it has become the default way serious AI work reaches production.
- How is a forward-deployed engineer different from a consultant?
- A consultant advises and hands over; a forward-deployed engineer builds and stays until it works. The practical test is commit access - if the person recommending the architecture is not the person shipping and maintaining it, the handoff risk the model exists to remove is still there.
- When does a company need forward-deployed engineers?
- When you know roughly what needs building and the gap is senior engineering capacity or AI expertise - a scoped workflow, an unproven technical path, or an internal build that stalled because the team who started it got pulled back to the day job. If the harder question is what to build, you need an AI Product Squad with product management and design as well.
- Should we hire our own AI engineers or engage forward-deployed engineers?
- Hiring your own works when you know what to build and can wait out the recruitment timeline. Forward-deployed engineers put senior capability in your team in weeks, with &above carrying the hiring risk and the management load - and because they build alongside your people, the capability stays in-house either way. Many clients do both: our engineers open up a capability, and their own hires then own it.
- Is forward-deployed engineering only for AI platform companies?
- No. Model providers embed engineers to get you live on their platform, so those architectures reasonably favour that platform. &above does not sell a platform - our forward-deployed engineers architect for your constraints, and the stack is chosen to fit your organisation rather than one provider.
- Which industries use forward-deployed engineers?
- Any industry where AI has to run against real systems and real data. We have embedded engineers in retail media at Tesco, in enterprise sales at Google Cloud, and across scale-ups and enterprises in finance, media and consumer technology. The model matters more than the sector - embedded delivery works wherever a handoff would fail.
- How does an engagement with &above's forward-deployed engineers work?
- We agree the workflow and the team shape, then embed senior engineers directly in your team - usually within weeks, not months. They work at your pace, in your tools, with &above handling hiring, management and quality. Engagements start with engineers against a scoped workflow and expand into a full AI Product Squad once the scope proves out.