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Forward Deployed Engineering
A Forward Deployed Engineer, Embedded in Your Team. Not a Consultant. Not a Deck.
A forward deployed engineer sits inside your team and ships production code instead of a recommendation. We place ours in 2-4 weeks, backed by a 90-day guarantee, to build and run the AI agents and automation your business actually needs.
Free 15-min call · Backed by a 90-day replacement guarantee
Operational focus
Embedded, not advisory
2-4 week placement window
90-day replacement guarantee
Engineers embedded with teams at
Why this role exists
The model came from Palantir, and frontier AI labs adopted it for the same reason.
Palantir embedded roughly 120 engineers directly with JPMorgan in 2009, because the data was too sensitive and the workflow too specific for a product handed off remotely (source). The pattern stuck: a model or a product alone does not produce a working result inside someone else's stack. Somebody has to sit with the mess and build the thing that closes the gap.
Frontier AI labs hit the same wall. OpenAI's own forward deployed engineering team grew from 2 to 39 people in a single year, targeting problems worth tens of millions to low billions, because roughly 95% of enterprise AI deployments never make it to production (source). In 2026, OpenAI went further and stood up a dedicated deployment company through the Tomoro acquisition, embedding engineers with adopters including HP, Intuit, Oracle, and State Farm (source).
We are not affiliated with and do not partner with Palantir or OpenAI. They are cited here as the origin and market context for the FDE model, not as our clients.
Further reading
For a deeper breakdown of the FDE build loop, read Vas's "FDE in 30 Days" guide at Varick Agents. It is the most detailed public roadmap of the audit-evals-deployment loop we describe below, written from the operator's side of the work.
What Our FDEs Actually Do
Real work mapped to real AY capability. Nothing invented, nothing outsourced.
Embed and learn the real workflow
We sit inside your team, not beside it. Before writing code, we map how the work actually happens today: the exceptions, the manual handoffs, the parts the org chart does not show.
Build against evidence, not vibes
We turn the messy, non-deterministic parts (what an AI agent should do) into a golden set of real cases with correct answers, so quality is measured, not assumed.
Ship to production and own the outcome
We build on your existing systems, deploy n8n workflows, AI agents, and Claude Code engineering into production, and stay accountable for whether it actually works.
Real Outcomes, Not a Demo
From our published case studies: the same automation and agent stack our forward deployed engineers build and ship.
Client Reviews
Teams That Ship With Our Engineers
Including Roald Larsen (CEO, Untaylored) and Connor Miller (Technical Director, Uniworx), two of the technical leads our engineers have embedded with directly.
Elie Salame
COO · Adstronaut.io
Elie Salame
COO · Adstronaut.io
How our FDEs build
Our engineers arrive with their agents.
Claude Code is the brain, MCP connects to your tools, evals catch what a demo would hide. One embedded engineer, working like three normal hires.
- 01Claude Codethe brain · subagents · MCP
- 02Anthropicmanaged agents · runtime
- 03CursorIDE pair-programming
- 04n8ncron · webhooks · glue
- 05E2Bsandboxed compute
FDE vs Consultant vs Staff Aug vs Agency
Four ways to bring in outside help. Only one is accountable for the outcome instead of the hours or the advice.
| Forward Deployed Engineer | Consultant | Staff Aug / Contractor | Traditional Agency | |
|---|---|---|---|---|
| What they deliver | Working software in production | A recommendation deck | Headcount, hours billed | A scoped project, then a handoff |
| Where they work | Embedded in your team | From the outside, then gone | Wherever you place them | Mostly remote, arm's length |
| Accountable for | The business outcome | The recommendation | The hours worked | The deliverable, not the result |
| How trust is earned | Evals on real data, shown to you | Credentials, case studies | Resume, interview | Portfolio, references |
| Engagement shape | Ongoing, judgment-heavy | Fixed-scope engagement | Time-based, open-ended | Fixed-scope project |
How the Engagement Works
A repeating loop: audit, evals, deployment. Each phase has to earn the right to the next.
1
Audit
Week 1We learn how the work really happens by sitting with the people who do it, not by reading the documented process. Output: a today-vs-with-AI operating map.
2
Evals
Week 1-2We build a golden dataset of real queries with hand-labeled correct answers, and track pass rates and failure categories before anything touches production.
3
Deployment
Week 2-3We build on what you already have, sandbox first, increase autonomy gradually, and monitor everything. No forced migrations.
4
Iterate and defend
OngoingWe document what v1 got wrong, how it improved, and report the outcome in terms a technical lead and a VP both trust.
When an FDE fits
- You have AI ambition but no in-house AI-native engineering depth.
- You need production code shipped in weeks, not after a long hiring cycle.
- You want one accountable engineer owning outcome, not a rotating cast.
When NOT to hire one
- You want a strategy deck and a roadmap, not shipped code -- that is AI strategy consulting, not an FDE.
- Your scope is small, fixed, and well-defined with no ambiguity -- a scoped project with an agency is cheaper.
- You need a large team of specialists across many domains at once -- an FDE is one deep generalist, not a department.
- You are not ready to give an engineer real access to your systems and data -- embedding does not work at arm's length.
What It Costs
Placement-based pricing, scoped to how much access and how many systems your engagement touches. Two honest starting points:
Single embedded FDE
From $60,000/year
One engineer, embedded full-time in your sprint, same nearshore rate as our engineer placement offer.
Scoped engagement
Custom, by call
Shorter or narrower engagements (a single agent, a single workflow) are quoted after the discovery call.
Placed engineers embedded with teams like the ones above · backed by a 90-day replacement guarantee.
Book a Free Strategy Call
Stop Interviewing. Start Shipping.
Tell us what needs to ship. We'll tell you honestly whether a forward deployed engineer is the right fit, and match you with one within the week if it is.
Book a 30min Free Strategy Call
In this call, we'll walk through your project scope, timeline, and goals - so we can both check if we're a fit. No obligation, no slide deck, just a working session.
Don't want a call? Email walid@ayautomate.com
Opportunity Map
Implementation Path
Fast Follow-Up
“The team is super fast - sometimes we had to slow them down. We managed to scale the company without investing into hiring.”
Elie Salame
COO, Adstronaut.io
Video Call
Phone Call
In-Person
We've created products featured in
Walid Boulanouar
View LinkedInForward Deployed Engineer
Tell us what needs to ship, then pick a time directly on this page.
Backed by a 90-day replacement guarantee
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FAQ
Forward Deployed Engineer Questions
What is a forward deployed engineer (FDE)?+
A forward deployed engineer is an engineer who embeds inside a customer's team and writes production code on top of a product, rather than advising from the outside. The model started at Palantir, where engineers embedded on-site with government and defense customers whose data was too sensitive to hand off remotely.
How is an FDE different from a consultant?+
A consultant is accountable for a recommendation. An FDE is accountable for the shipped result. FDEs write production code themselves, embed for weeks at a time, and use evaluation-driven development instead of a slide deck to earn trust.
How is an FDE different from staff augmentation?+
Staff augmentation places a person against your hours and org chart. An FDE is placed against an outcome: a workflow that needs to run, an agent that needs to hit a quality bar. Same embedded model, different accountability.
Do I need a forward deployed engineer or an AI strategy consultant?+
If you already know what you want built and need someone to build and ship it inside your stack, that is an FDE. If you need help deciding what to build first, start with AI strategy consulting, then bring in an FDE to execute.
How fast can AY Automate place a forward deployed engineer?+
Our standard placement window is 2-4 weeks from requirements to a working engineer inside your sprint, backed by a 90-day replacement guarantee if the fit is wrong.
The FDE knowledge hub
What is an FDE?
The full definitional guide: what the role is, and when to embed one.
FDE in 30 Days
The audit, evals, deployment methodology, week by week.
Salary Explorer
Filterable, cited compensation ranges by region and experience.
Engineer Placement
The placement mechanics: how we match, place, and guarantee.