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Forward deployed engineering

Forward deployed engineers, inside your team

A forward deployed engineer is a software engineer who works inside a customer’s team, on that customer’s systems, until a capability runs in their daily work. For most businesses today that capability is AI. predeploy rents you forward deployed engineers to get AI working before you scale it, and helps you decide when to hire your own.

Book a call →Hire or rent?

OpenAI describes FDE ownership from discovery through production rollout. OpenAI: forward deployed engineer role ↗

Anthropic describes engineering embedded with customer teams. Anthropic: staff forward deployed engineer role ↗

Roles on fractional.quest · checked 26 Oct
30Open roles
20Posted in the last 7 days
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£500/day£2,000/day
EngagementFractionalPosted 24 Oct
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EngagementInterimPosted 23 Oct
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£500/day£2,000/day
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£500/day£2,000/day
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£500/day£2,000/day
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EngagementInterimPosted 22 Oct
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EngagementFull timePosted 22 Oct

AI, go-to-market and fractional leadership roles listed on our sister site, Fractional Quest, read from each role’s public listing on 26 Oct 2026.

What an FDE is

Forward deployed engineers: what do they own?

Forward deployed engineering puts an engineer next to the people doing the work, rather than behind a product roadmap. They learn the process first-hand, build against real data and stay until the thing runs.

The role grew up in companies that sell complex software into large organisations, where the gap between what the product can do and what the customer gets from it is closed by engineers on the customer’s side. AI has the same gap, which is why the role now turns up far beyond those companies.

That is also where our name comes from. predeploy deploys engineers before you scale, so what you scale already works.

A consultant tells you what to build. A product engineer builds it for everyone. A forward deployed engineer builds it for you, in your systems, and hands it to your team.

The model

Forward deployed engineers: how does the model differ?

Flip the switch to compare an engineer working on a vendor’s roadmap with one working inside your team.

The bars compare approach, not measured results.

Where the engineer worksOn the vendor roadmap
What they build againstSample data
Who learns the processA product manager
When it is doneWhen it ships

Why now

Forward deployed engineers: why consider an engagement?

The UK AI sector has grown fast, and the people who can build with AI are in short supply. Renting an engineer is one way round the queue.

The DSIT, Artificial Intelligence sector study 2024 counted 5,862 AI companies in the UK in 2024, up 58% in a year. Among organisations in the AI labour market, the DSIT, AI Labour Market Survey 2025 found that 57% report a technical skills gap.

5,862AI companies identified in the UK in 2024, up 58% on 2023

Source: DSIT, Artificial Intelligence sector study 2024 (Sep 2025)

86,139people in AI-related roles at UK AI companies in 2024, up 33%

Source: DSIT, Artificial Intelligence sector study 2024 (Sep 2025)

57%of organisations in the UK AI labour market report a technical skills gap

Source: DSIT, AI Labour Market Survey 2025 (Jan 2026)

When you need one

Forward deployed engineers: when do you need one?

You do not need a forward deployed engineer to try AI. You need one when trying has stopped turning into anything that runs.

  • Pilots that never ship. Proofs of concept work in a demo and stall before production.
  • Tools nobody uses. Licences were bought, but the work happens the old way.
  • No one owns it. IT is busy, the business has no engineers and the vendor has moved on.
  • Data in many places. The useful information sits across the CRM, inboxes, files and spreadsheets.
  • A deadline. The board wants AI working this year, not a plan for next year.

Hire or rent

Forward deployed engineers: hire or rent?

Tick what applies and the needle shows which route fits. The verdict is a starting point for a conversation, not a rule.

Rent an FDEFractional leaderHire full time

Tick what applies

The needle moves with each answer.

Renting through us suits a business that needs AI working now. You get engineers who have done the work before, without a search, a notice period or a permanent headcount decision made too early.

Hiring suits work that is permanent and core to the product. If you want to hire permanently, or the real gap is a leader, our sister company Fractional Quest is a fractional recruitment agency. It can place a fractional CTO to own the technical roadmap, or a fractional COO when the operating model has to change around the new workflows.

Engineers and technical leaders looking for this kind of work can browse open fractional jobs on Fractional Quest.

Time to start

Building before a hire would start

A permanent hire takes a search and, usually, a notice period before any work begins. A rented engineer starts on the work.

Rent an FDE from predeployBuilding
Scope · about 1 wkEmbed and build · ongoing
Hire a permanent FDEStarting
Define role · 1–3 wkSearch and interview · variesNotice period · often 1–3 months

Illustrative plan, not measured data. Search and notice periods vary widely; we give you a real start date after the scoping call.

How we work

Assess, build, train, hand over

Our forward deployed engineers follow the same four stages on every engagement. Each one ends with something your team can use.

Assess

Learn the work first-hand

The engineer sits with the people who do the job, maps the process and finds the steps AI can take over safely.

Build

Ship against real systems

They build inside your stack, with your data, in short loops your team can see and test.

Train

Teach the people who use it

Users learn the workflow; someone on your side learns to maintain and extend it.

Hand over

Step back, on purpose

Documentation, named owners and a list of what to build next. We leave when your team can run it.

A practical next step

Forward deployed engineers: how do you commission the work?

Give the engineer a bounded workflow, a counterpart in the business and evidence for accepting the result.

Read the forward deployed engineering overview for the model and responsibilities. Then compare hiring forward deployed engineers with an embedded delivery engagement. The distinction matters: permanent headcount needs a continuing roadmap, while an engagement needs a clear first outcome and exit arrangements.

An illustrative first brief could ask for a reviewed account research pack from approved sources, with links back to the evidence. It should also say what happens if sources conflict, a record is missing or an integration fails. A demo of the happy path does not answer those questions. Use an AI evaluation consultant to define acceptance cases and deployment services to prepare the release and fallback.

For permanent recruitment, the FDE hiring guide covers discovery exercises, code review and delivery judgement. For either route, agree who owns the repository, credentials, operating costs and user support. Pair the handover with training and consultancy so an internal owner can run a routine update without relying on the engineer to remain indefinitely.

For a specific build, compare agent development with chatbot development. Prepare an AI knowledge base only from suitable sources, and use LLM evaluation to preserve evidence for the release decision.

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Choose your next step

Explore the AI implementation library

Start with a topic overview, or go directly to the question your team needs to answer.

Forward deployed engineering

Understand the role, compare hiring routes and commission an FDE.

Start with the decision

More questions in this topic

FAQ

Questions, answered

A forward deployed engineer (FDE) is a software engineer who works inside a customer’s team, on that customer’s problems and systems, instead of on a product roadmap back at base. The job is to turn a capability, today usually AI, into something running in the customer’s day-to-day work.

Forward deployed engineering is the practice of putting engineers where the problem is. They sit with the people doing the work, learn the process first-hand, build against real data and systems, and stay until the thing runs. It sits between consulting, which advises, and product engineering, which builds for many customers at once.

A solutions engineer mostly helps sell a product and set it up. A consultant mostly advises. A forward deployed engineer writes and ships the code that makes the capability work in your business, and is judged on whether it runs, not on the advice or the sale.

Rent one when you need AI working now and are not yet sure how much ongoing engineering it needs. Hire when the work is permanent and core to the product, and you can give the person a long roadmap. Many companies rent first, learn what the role is, then hire. The hire-or-rent section above goes through it.

Because the work happens before you scale: we deploy engineers into your team to get AI working on a small number of workflows first, so that what you scale is something that already runs. The name and the forward-deployed model are the same idea.

Where it helps, yes, and remotely the rest of the time. Forward-deployed means working inside your team and your systems; the exact split between office and remote is agreed per engagement.

We quote per engagement after a scoping call, once we know the workflow, your systems and how much of an engineer’s week it needs. The first call is free: book it here.
Forward deployed engineers | Predeploy AI
Forward deployed engineers — Predeploy AI

Taking briefs this week

Tell us what is stuck. We send an engineer.

A call with an engineer, not a sales team. Bring the process that eats your week and we will tell you, on the call, whether AI can take it.

01We map the use cases worth building first

02We say plainly what AI will not fix

03You leave with a scoped first build and a timeline

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