Hire a forward deployed engineer who ships inside your systems
AI projects rarely fail on the model. They fail in the last mile — the data that lives in three places, the legacy system nobody documented, the workflow your team actually uses instead of the one on the process map. I embed with your team, work against the real thing, and leave production software behind.
What a forward deployed engineer actually does
A forward deployed engineer is a software engineer who deploys with the customer instead of behind a roadmap. Same job title Palantir made famous and AI labs now hire for in volume, and for the same reason: models and platforms are commodities, but getting one to work against a specific company's data, constraints, and habits is bespoke work that can't be done from a distance.
In practice that means sitting with the people doing the work, learning enough of the domain to have an opinion, writing code against production data on day one, and staying accountable until the thing is running — not until the invoice clears.
Signs you need an FDE, not another vendor
The demo works, production doesn't
The proof-of-concept impressed everyone on clean sample data. Then it met your actual records — inconsistent, half-migrated, full of exceptions — and the accuracy fell off a cliff.
Your data isn't where the vendor assumed
It's split across an ERP, a decade-old SQL box, a shared drive, and someone's spreadsheet. Every integration quote you get comes back vague because nobody wants to touch it.
You bought AI tooling nobody uses
Licenses are paid and adoption is near zero, because the tool doesn't fit the way the work actually happens. That's a workflow problem wearing a software costume.
Nobody speaks both languages
Your domain experts can't scope the model work, and your engineers can't evaluate whether the output is right. The translation layer is missing and it's costing you months.
You need someone in the room
The requirements only surface when you watch the job get done — on the floor, in the truck, at the counter. That discovery can't happen over a status call.
You're the vendor and the account is stuck
You sell an AI product and one customer needs an engineer on the ground to get it live. I take subcontracted deployments under your name.
How the engagement runs
Ride along
I sit with the people doing the work and map where the hours actually go — not where the process document says they go. Output is a short, prioritized list of what's worth building, what isn't, and the rough payback on each.
Ship one narrow thing
One real slice, in production, against real data — early enough that you can judge the engagement on working software instead of a status report. Narrow on purpose: a small thing in production teaches you more than a broad thing in staging.
Widen and harden
Expand coverage, handle the exceptions the first slice exposed, add evals and instrumentation so you can see whether it's still working next quarter. This is where most AI pilots quietly die and where the real work lives.
Hand off deliberately
Documented, tested, running in your infrastructure, owned by your team. No proprietary wrapper, no lock-in, no standing retainer required to keep the lights on. If you want me back for the next problem, it should be because it went well.
What I bring to the deployment
Eighteen-plus shipped projects across seven industries — healthcare, physical security, govtech and compliance, e-commerce, logistics, marine, and directory platforms. The range matters for this role: deploying into someone else's environment means the domain is always new and the constraints are never the ones you expected.
AI & ML in production
LLM pipelines and agents, RAG over messy internal corpora, evals, computer vision (YOLO, OpenCV, ONNX), on-device inference where the network can't be trusted.
Integration into legacy systems
ERP and catalog systems, Magento, carrier and device APIs (UPS, ONVIF), CAD/DXF parsing, OAuth handlers, and the undocumented internal service nobody wants to own.
Full-stack delivery
Next.js, React, React Native, Flutter, Python and FastAPI, Node. Web dashboards, offline-capable field apps, internal tools — whatever the workflow actually needs.
Ops that survive handoff
GCP, AWS, Vercel, Docker, Cloud Run, PostgreSQL, Firebase. Deployed on your infrastructure, with the runbook written before I leave.
Built for environments that fight back
Each of these required learning an unfamiliar domain and shipping against constraints that only showed up on site.
Where I work, and how we'd start
- Remote-first, on site when it counts. Kickoff, discovery workshops, and go-live in person; day-to-day delivery in your Slack and your repo.
- Direct or subcontracted. I take engagements directly with the company deploying AI, or under an AI vendor's name into their customer accounts.
- Scoped up front, not billed by the hour. You get a number and a defined first slice before work starts.
- One engagement at a time where it matters. Forward deployed work doesn't survive being someone's fourth priority.
Hiring a forward deployed engineer — common questions
What is a forward deployed engineer?
A forward deployed engineer (FDE) is a software engineer who works inside the customer's environment rather than behind a product roadmap. They sit with the people doing the work, learn the domain, write code against the real data, and stay until the thing is running in production. The role was popularized at Palantir and is now standard at AI labs and enterprise AI vendors, because the hard part of shipping AI is almost never the model — it's everything around it.
How is an FDE different from a consultant or a staff-aug contractor?
A consultant delivers a recommendation and leaves. A staff-aug contractor waits for tickets and builds what the ticket says. A forward deployed engineer does the diagnosis and the build — finds the actual bottleneck by watching the work happen, then ships the software that removes it. You get an engineer who is accountable for the outcome, not the deliverable.
What does it cost to hire a forward deployed engineer?
Engagements are scoped and priced up front rather than billed hourly, so you know the number before work starts. Cost tracks the size of the first slice and how long you want me embedded. The contact form asks for a budget range so we can size that first slice realistically on the initial call — if the scope doesn't fit the budget, I'll say so instead of stretching it thin.
Do you work on site?
Remote-first, on site when it counts. That usually means the kickoff, discovery workshops, and go-live, plus any point where the problem is physically in the room — a warehouse floor, a field install, a control room. Day-to-day delivery runs remote, in your Slack, on your board.
Can you work alongside our existing engineering team?
Yes, and that's the common case. I work in your repo, your branch conventions, your review process. The goal is that your team owns the code when I leave — documented, tested, and handed off deliberately. No black boxes, no dependency on me to keep it running.
What if we don't know what to build yet?
That's a normal starting point and it's the first week of the engagement, not a blocker. Discovery is ride-along: I sit with the people doing the work and map where time actually goes. You get a prioritized shortlist of what's worth building, what isn't, and what the payback looks like — and you can stop there if the answer is 'nothing yet.'
Do you take subcontracted or white-label FDE work for AI vendors?
Yes. If you're an AI product company that needs an engineer deployed into a customer account — implementation, integration, custom connectors, on-site enablement — I take that work under your name or mine, whichever the account calls for.
Tell me what's stuck. I'll tell you if I can fix it.
Describe the workflow, the system, or the pilot that stalled. If a forward deployed engagement isn't the right shape for it, I'll say so on the first call and point you somewhere better.