Forward Deployed Engineers: frequently asked questions
enterpriseai
What a Forward Deployed Engineer actually does, how they differ from consultants, and what to expect week by week during an enterprise AI engagement.
Forward Deployed Engineers (FDEs) are the model Palantir and the frontier labs use to make enterprise AI actually land. Below are the questions leaders ask before hiring one.
What is a Forward Deployed Engineer?
An FDE is a senior engineer who embeds inside your team to design, build, and ship an AI system against your real data and workflows — then trains your people to own it. They are not a sales engineer, not a solutions architect, and not a staff-aug contractor.
How is this different from a consultant or a systems integrator?
Consultants deliver slides and SIs deliver tickets. An FDE delivers a running system, evaluation harnesses, and a team that can extend it after they leave. Success is measured against a pre-agreed baseline, not billable hours.
What does a typical engagement look like?
Four moves: assess candidate use cases, architect the approach across the ten engineering principles, deploy with your team, and verify against baselines. Most engagements run six to twelve weeks per workflow.
Do we need to hand over our data?
No. Engagements can run on SNAP Enterprise with self-hosted models and enterprise memory inside your perimeter. See how to roll it out without losing control and the self-hosted AI FAQ.
What technical ground do FDEs cover?
Serving architecture, caching, model choice, output reliability, agent budgets, grounded retrieval, evaluation, observability, and safety. The full breakdown is in the Silverberry AI Engineering Guide.
How is ROI verified?
Against the baseline captured during assessment — cost per task, cycle time, error rate, or whatever the sponsor picked. If the number isn't there, we iterate before scaling.
How do we get started?
A thirty-minute AI ROI Roadmap session identifies the first workflow worth deploying against.