---
title: "Forward Deployed Engineers for Hire — Edgeware Global"
description: "Senior forward deployed engineers who embed with your users and data, write production code inside your systems, and own the outcome until it is in daily use."
url: https://edgeware.global/forward-deployed-engineers/
---

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# Forward Deployed  
_Engineers_

One senior engineer, embedded with your users and your data, who owns whether the system works — not a ticket queue. The model AI companies use to get software working inside real businesses, available without hiring one.

[Talk to us about an FDE](https://edgeware.global/contact-us/) [How it works ↓](#how-it-works)

When you need one

## The code is not the problem.  
_Everything around it is._

### The demo works, production doesn't

An AI feature that handled the test cases falls over on your real data, your real volumes, and the edge cases nobody listed.

### It's live, but nobody uses it

A platform you bought or built is switched on, and the team still works in spreadsheets. Adoption is the project now.

### Requirements only exist in people's heads

Nobody can write the spec from the outside. Someone has to sit with the people doing the work and find out.

### Three teams, no owner

The work crosses departments and every team owns a piece. An FDE owns the outcome across all of them.

### Legacy systems in the way

The ERP, the undocumented API, the database only one person understands. Integration is where these projects stall.

### Your engineers keep getting pulled in

One integration keeps dragging your best people off the roadmap. An FDE takes it off their plate and finishes it.

How an engagement runs

## Built to finish,  
not to _renew._

I

### Weeks 1–2: map

Architecture, data, workflows and the people involved. We say out loud what is really in the way.

II

### Agree the outcome

Milestones set at the outcome level — "invoices processed without re-keying" — not as a list of tickets.

III

### Build inside your systems

Production code in your repo: integrations, pipelines, agent orchestration, the unglamorous glue.

IV

### Adoption & handover

We stay until it is in daily use, then hand over documentation, tests and a team that can run it.

Choosing a model

## FDE, consultant, or _new hire?_

Three common ways to get an AI or integration project working. They differ in who is accountable when it reaches production.

| Option | What you get | Accountable for | Trade-off |
| --- | --- | --- | --- |
| **Forward deployed engineer** | A senior engineer embedded with your users and data | The system working in live use | Costs more per person than augmentation; knowledge sits with one or two people until handover |
| **AI consultancy** | Strategy, use-case selection, a report or a pilot | The recommendation | Implementation and integration are left to someone else |
| **Full-time hire** | A permanent senior engineer on your team | Whatever you assign | Months to hire, and the role may outlast the project |

If you already know exactly what to build and simply need more hands, you do not need an FDE — [team extension](https://edgeware.global/team-extension/) is the cheaper model. We will tell you which fits.

![Engineer working with a client team](https://edgeware.global/images/we-work-like-part-of-your-team.webp)

## Why one engineer _is enough now._

The role was popularized at Palantir and became standard at AI companies such as OpenAI, Anthropic and AWS, because on a real rollout the hard part is rarely the model. It is the messy data, the integration nobody documented, and the workflow that exists only in someone's head.

With coding agents doing much of the typing, one senior engineer who understands the business can now deliver what used to need a small team. What cannot be automated is judgement, context and accountability — which is exactly what an FDE brings.

Our FDEs are senior engineers with 5+ years of production experience, backed by our CTO's twenty years of building systems. See [how we build with AI](https://edgeware.global/how-we-build-with-ai/).

Common questions

## What clients usually _ask first._

What is a forward deployed engineer?

A senior engineer who embeds inside your environment and owns an outcome end to end: framing the problem with the people who do the work, writing production code inside your systems, wiring it into your real data and legacy tooling, and staying until it is in daily use.

How is an FDE different from an AI consultant?

A consultant recommends and leaves; the implementation is someone else's problem. An FDE writes the production code, owns the integration, and is measured on whether the system works in live use. Many of our engagements start where a consulting report ended.

Will an FDE replace our in-house team or AI platform?

No. The usual job is to make what you already have work: your in-house engineers, the AI platform you licensed, the data you already collect. An FDE fills the gap between them and leaves your team able to run the result.

How long does an FDE engagement last?

Typically three to six months for one outcome. It is designed to end: milestones are set at the outcome level, and the last weeks are handover — documentation, tests, and your people running it without us.

Do your forward deployed engineers work on site?

Usually not. Embedded means embedded in your systems and your working week — your Slack, your repo, your standups and the calls with the people who will use what gets built — with a genuine timezone overlap. Where physical presence matters, such as a regulated environment or a kick-off week, we arrange travel case by case.

How is it priced?

As a monthly engagement on the same commercial terms as our team extension work: you interview and approve the engineer, you own the IP, and you can scale or end it with 30 days' notice. An FDE costs more per person than straight staff augmentation, because you are buying ownership of an outcome rather than capacity.

## Have a project that's stuck  
in the _last mile?_

[Talk to us about an FDE](https://edgeware.global/contact-us/)