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Building a strong FDE team is more than having a group of senior engineers who can work closely with business teams. Without a clear mission, decision-making authority, defined boundaries, and a structured way to feed deployment lessons back into the product, even experienced engineers can end up operating like an expensive consulting group.
Should companies build the forward deployed engineering capability in-house, outsource it, or skip it altogether? And if the model fits your business, how should the team be structured, which projects should it own, and what prevents it from becoming a permanent custom development unit?
This article walks through the operating model behind successful FDE teams. We’ll explore how to assemble the squad, choose the first project or deployment that the new FDE team will work on, set technical guardrails, measure outcomes, and decide whether to hire internally or use an outside partner.
Key takeaways:
A forward deployed engineering team is a multidisciplinary technical squad that works directly with internal business units or external customers to fill in the blanks between product development and real operating environments. Its work typically spans:
What sets the team apart isn’t the list of responsibilities but the way it operates. The team has shared ownership of the outcome, and the squad stays involved throughout deployment, bringing technical and product insights back for reuse.
The job description and scope vary by organization. For instance, OpenAI explicitly describes its FDEs as owning discovery, technical scoping, system design, build, and production rollout, in collaboration with customer engineering and domain teams1.
Other organizations may take their consulting roles and simply rebrand them as forward deployed engineer roles, as long as those employees have the coding talent, technical delivery, and product decision savvy to back it up.
There’s nothing objectively outdated about traditional software delivery. It remains the right choice for many projects and works well when the team knows exactly what it needs to build and can plan out the work before starting development. But that can be more difficult when there’s operational uncertainty.
Forward deployed engineering addresses a different type of problem. It’s designed for cases when important decisions can’t be made until engineers work alongside users, understand operational workflows, and see how the software behaves in a live environment. The difference between the two is the operating model.
| Operating dimension | Traditional software delivery | Forward-deployed engineering |
| Operating environment proximity | Teams gather context before or between delivery cycles | Engineers remain closely involved with the operating environment throughout delivery |
| Discovery process | Scope is defined as thoroughly as possible before development | Discovery continues as the team learns from the live environment |
| Feedback loop | Feedback may pass through multiple entities, including product, support, delivery, and account teams | Engineers bring their own field observations back for technical and product discussions on reuse |
| Deployment ownership | Engagement may end at launch or handoff | The team stays tied to the product, its adoption, and the outcome |
| Success metrics | Scope completion, software quality, velocity, and release predictability | Time to value, adoption, integration quality, product learning, and handoff readiness |
| Best fit | Stable requirements and predictable implementation | Complex or uncertain workflows, and high deployment risk |
Neither approach is inherently better. Both models reflect a high level of engineering quality and are meant to solve a different type of delivery problem. It depends on how much uncertainty exists between designing the solution and deploying it in production.
What conditions justify building a forward deployed engineering team? Overall, when uncertainty continues well beyond the planning stage.
An FDE function is most useful when several of the following signals are present:
Building an AI FDE model might be more complex than necessary if your company is consistently working with the same requirements, especially if rollouts follow a standard process. A regular product or delivery team might be able to take on that work without unnecessary overhead and may be the simpler and more cost-effective choice. Alternatively, external FDE services are an option for temporary engagements.
Oxagile provides the engineers, architects, and delivery support needed to deploy and manage complex software in the field.
There isn’t a specific organizational chart for a forward deployed squad. Team composition depends on the engagement scope, deployment complexity, and the expertise required.
If you’re working on a contained pilot, you might need a single FDE lead, a senior engineer, part-time product support, and access to an architect. On the other hand, if you’re performing a larger enterprise rollout, you might add dedicated specialists for each technical or industry-specific discipline.
The lead keeps the project focused on the final result. They’ll be in charge of guiding the technical work, making major decisions, communicating with the decision-makers, and keeping the team on the same page.
These are the people building and fixing the solution, writing code, working with data, troubleshooting software issues, and getting all the product components up and running.
The architect looks at how new work fits into the customer’s system. If the team needs help making sound technical choices, they’re the person to turn to.
Product leadership helps the team distinguish between which components are specific and which ones have repeatable functionality. They also decide what should ultimately be added to the main product roadmap.
These are all the additional subject-matter specialists that might be on the project:
These specialists join the squad when the engagement requires expertise beyond the core team. Not every deployment needs every role, and many responsibilities can be combined in smaller engagements.
If there are several squads active at once, companies typically look to a forward deployed engineering manager to oversee the project and make sure the operating model is healthy.
An embedded squad shouldn’t become a standalone custom development organization. Its role is to solve deployment-specific challenges, continuously strengthening the product for future implementations.
That only happens when lessons from individual engagements flow back into the core product organization. Integration patterns, reusable components, architectural improvements, recurring workflow gaps, and documentation shouldn’t remain tied to a single deployment.
Palantir is the prime example of a company that does this really well by splitting the role into two: Devs and Deltas. Devs build capabilities that serve many customers, while Deltas apply multiple platform capabilities to an individual customer problem2.
The Palantir model succeeds because deployment experience flows back into product development instead of remaining isolated within individual engagements. That means they need to have a regular process in place for sharing and reviewing those lessons, such as:
With these processes, the embedded team has more room to solve the immediate deployment problems without creating a separate product organization. The core product team, on the other hand, still owns the shared platform and decides which lessons to apply to it.
Building a forward deployed engineering team starts long before the first hire. Define the team’s mission, authority, operating model, and success criteria first. Recruiting should follow those decisions.
Why does the team need to exist in the first place? What problem is it trying to solve?
The mission should define why the team exists and, just as importantly, what falls outside its scope. Maybe something like: “Accelerate complex deployments and feed reusable improvements back into the product.”
A clear mission prevents the FDE team from becoming a destination for every difficult implementation or last-minute customer request. You can then answer:
Embedded teams lose much of their value if every decision has to pass through several departments. It should be able to make independent decisions within agreed architectural and product boundaries. That might include:
In the same way, the team should also have a clear escalation path if there are decisions that impact things like security, shared infrastructure, the product roadmap, or contractual scope.
Start with an engagement that’s large enough to test the operating model. A strong pilot usually has:
Avoid choosing a pilot that has open-ended customization or no path to reuse.
Large teams can cause coordination problems if the operating model hasn’t been tested yet. Go with a smaller pilot team to start and add specialists when the work calls for them. This might look like:
Unlike a traditional engineering backlog, an FDE backlog should capture both technical work and operational outcomes. Each major item should connect:
With this structure, the team always has access to times when technically complete features failed to improve operations.
Architectural guardrails help the team move quickly without creating one-off solutions that make the product harder to manage. It’s up to you to define:
The squad should also know which requests require a product decision. Requests that appear across multiple deployments are often signals that a capability belongs in the core product.
Every delivery cycle should answer a few practical questions to keep discovery connected to delivery:
Velocity tells you how much work the team completed. But it’s also important to track whether the software went live, users adopted it, the work could be reused, and ownership transferred smoothly. Some useful FDE metrics in this case might include:
As the team grows, the head of forward deployed engineering can oversee hiring, set team standards, and decide which projects take priority.
Assembling an internal FDE team makes most sense when you want such engineering to be a long-term part of the business and there are enough projects to keep the team busy to justify the full cost of hiring forward deployed engineers.
If you want to test out the model and support a few major deployments without setting out to build a permanent department, having a partner that offers forward deployed engineering services can be a better starting point. Many companies follow a hybrid path: they launch the first engagements with an experienced partner, establish operating practices, and gradually bring more of the work in-house over time, as it implies the lengthy process of recruiting, management, training, and knowledge transfer.
Oxagile can help assemble a forward deployed engineering squad for complex product deployments, AI rollouts, fintech integrations, video modernization, and enterprise implementation work.
1. Forward Deployed Engineer (FDE) – SF — OpenAI
2. Dev versus Delta: Demystifying Engineering Roles at Palantir — Palantir
This model works well when the project involves complex integrations or when workflows aren’t clear. The model is also useful when engineers need to learn from the live environment as they build.
Most engagements last a few months, though the timeline depends on the scope. A single rollout may wrap up after launch and handoff.
The FDE team brings back what it learns from the project. Product and engineering leaders can then decide which components and ideas should become part of the main product.
The contract should spell out who owns the code, documentation, integrations, and reusable components before starting any work. It should also cover access to data and responsibility for anything created during the engagement.
Use one when the current team can’t fully plan the work until engineers see the systems and workflows up close. If there is a clear scope and the rollout follows a standard process, you may only need a delivery team.
It depends on the project and the skills required. Oxagile begins with discovery and team planning, then brings in the engineers, architects, DevOps specialists, QA support, or domain experts needed for the work.
