The seven skills that define a forward-deployed engineer

Getting AI projects into production takes more than a strong model. Someone has to understand the customer's business, connect the technology to the systems the team relies on, and build something people will actually use.

That's the forward-deployed engineer's job, and it calls for an unusual mix of skills. An FDE needs the technical background of a senior software engineer and the judgment to work with non-technical stakeholders on business problems nobody has fully defined.

Below, we break down the seven forward-deployed engineer skills we assess in every FDE, what each looks like in practice, and how to test for them.

Why do FDE skills differ from other engineering roles?

A software engineer on a product team usually works in their own company's codebase, building features for the product roadmap. The core product is theirs, and so are the systems around it.

A forward-deployed engineer works in someone else's environment. They connect AI to enterprise software they didn't build, work with data they didn't design, and pass a security review they don't control. The job ends when the system is running and the customer's internal team can own it.

That's the core of the FDE model. Forward deployment means the engineer stays with one deployment from discovery to handoff.

In practice, FDE work is part engineer, part consultant. AI labs and other AI companies hiring for the role ask for both in their job postings. OpenAI's FDE job posting, for example, asks candidates to "write and review production-grade code" and to "embed closely with customer teams." In Bloomberry's analysis of 1,000 FDE job postings, 47% explicitly required customer-facing ability.

^{For a full introduction to the role, read What Is a Forward-Deployed Engineer?}

What skills does a forward-deployed engineer need?

The first six skills cover the engineering. The seventh covers the judgment that ties them together.

1. They write production code that runs for years

FDEs write code that has to keep working after they leave. That usually means typed Python and solid SQL, including window functions and the kind of queries real reporting needs. TypeScript shows up often too, because FDEs build the small interfaces people use to review outputs and approve actions. In Bloomberry's analysis, 66% of postings mentioned Python and 35% mentioned TypeScript.

Perfect code matters less than maintainable code. FDEs add the tests, error handling, and logging the customer's team needs to keep the system running.

2. They connect AI to the systems the business already uses

An AI system is only as useful as what it connects to. FDEs work with REST and GraphQL APIs, webhooks, OAuth, and SAML to plug new tools into existing enterprise software.

Integration is as much about people as protocols. A strong FDE knows how to get through identity and access rules without waiting six weeks for a permissions request.

3. They make messy data usable

FDEs build data pipelines on platforms such as Snowflake, Databricks, and Postgres, and they set up vector search for systems that answer questions from company documents. Then they tune retrieval until it returns the right document for the question being asked. Users expect search over company documents to work as well as Google Search, so retrieval quality decides whether people trust the system.

Data quality is rarely what the project plan assumes. In a supply chain project, for example, inventory data might sit in three systems with three different product codes. FDEs find those gaps early, trace each problem to its root cause, and fix what they can before it reaches the model.

4. They build agentic systems around the model

Prompt engineering is only a small part of applied AI. FDEs design agent workflows with tool calls, memory, and fallbacks for when the model gets something wrong.

Model selection is part of the skill. Frontier models are worth their cost for complex, multistep work, while cheaper models can handle routine steps. An FDE weighs those trade-offs for each step of a production agent. At product companies, FDEs also share what they learn in the field with product managers, so the core product improves for every customer.

^{For more on routing work across models, read How to build an LLM gateway for cost, quality, and resilience.}

5. They prove the system works before people rely on it

Without measurement, "the AI works" is just an opinion. FDEs build an evaluation set from real examples of good and bad outcomes, then measure accuracy and cost before launch. After launch, they watch for hallucinations and quality drift.

Guardrails cover the cases the model will get wrong, including human review for high-stakes decisions and clear escalation paths for exceptions.

6. They deploy inside locked-down environments

FDEs deploy inside the customer's cloud, whether that's AWS, Azure, or Google Cloud, often using Kubernetes and identity and access management (IAM). Enterprise environments are locked down on purpose. FDEs need enough system design and distributed systems experience to work within those controls and pass security review before real users touch the system.

7. They frame the problem before they build

The last skill is discovery and judgment. FDEs sit with the people doing the work and the industry experts who know the domain, whether at client sites or on a customer call, and watch how the job actually runs. They notice the workarounds, the stalled approvals, and the decisions nobody has documented.

From there, they work with business owners on problem framing: what result matters, how to measure it, and where AI is the wrong tool. Clear communication matters here, because FDEs have to explain technical trade-offs to people who will never read the code. This is the part of the customer work that never shows up in a ticket.

Which FDE skill is hardest to find?

The hardest skills to find in FDEs are typically discovery and judgment. Coding agents now help with much of the first skill on this list, and many strong engineers can learn the integration and data work. Fewer have a deep understanding of how a business process actually runs, or the work style to sit with users until the system fits.

It also helps to know where nearby roles overlap. A solutions engineer may have the customer skills but less experience taking a system into production. Customer success and business development teams build strong relationships but don't usually write the code. A product engineer may have the technical skills but less experience working with customers. Forward-deployed work sits where those strengths meet.

How should you assess forward-deployed engineer skills?

Whether you're a startup CTO hiring your first FDE or an enterprise leader building a team, a résumé won't show most of these skills. In FDE interviews, look for evidence of real delivery:

• Ask to see actual work. A recorded coding assessment shows more than a list of languages.
• Ask what they shipped to production in the last year. Demos and proofs of concept don't count.
• Ask what happened after launch. Strong candidates can explain how they measured results, handled failures, and handed the system to an internal team.
• Listen for discovery. Notice whether they ask about the users and the business process, or only about the tech stack.

Few candidates will be equally strong in all seven areas. Decide which skills matter most for your first FDE engagement, and test for those directly.

How can engineers build FDE skills?

For engineers considering an FDE career, the biggest gaps are usually on the customer side. A practical FDE roadmap starts with the skills that are hardest to learn on the job:

• Ship one system end to end. A side project that takes an AI agent from prototype to production, with an evaluation set, logging, and handoff notes, shows more FDE skill than another demo.
• Practice discovery. Spend time with a support, operations, or sales team, and map how a real process works before proposing a fix.
• Learn the enterprise plumbing. Get comfortable with SQL window functions, OAuth, and cloud IAM, since they come up in nearly every deployment.
• Write for non-technical readers. Explain a technical trade-off in one paragraph a business owner could act on.

How FullStack can help

Every FDE we place is assessed on these seven skills, including a recorded, graded coding challenge you can review. Our nearshore FDEs work in US time zones, embed with your team, and take a working AI system into production in your environment. We send a shortlist within 48 hours of your first call.

^{Want the full picture? Download our free white paper, The Forward Deployed Engineer: A practical guide for AI and engineering leaders.}