What Does a Forward Deployed Engineer Actually Do?

Forward Deployed Engineers (FDEs) are professionals who specialize in software, machine learning, and systems integration. They collaborate with stakeholders to transform a vague business problem into functional software that aligns with an existing ecosystem. This role offers developers an opportunity to engage in both technical and human-centered work, making it a promising field for the future as companies transition from AI prototyping to embedding AI within products and workflows.

An FDE's primary responsibility is to engage with end users to design, prototype, integrate, and iterate upon a solution tailored to a specific need. The term "forward-deployed" emphasizes the importance of the FDE working closely with the team or customer in the field, as opposed to working solely on a central product backlog. By doing so, they gain a deep understanding of the unique constraints of that environment, such as data availability, approval workflows, API rate limits, auditing requirements, and potential failure modes specific to the domain.

The four-part FDE delivery loop consists of discovery, shipping the first version, integrating the solution, and enabling the solution. During the discovery phase, the FDE identifies the root problem by focusing on the business process rather than the AI model itself. For instance, instead of asking "how can we use an AI agent?", the FDE asks "what decision or process is currently slow, error-prone, or expensive, and how could a better outcome look?"

This iterative process results in a clear problem description that includes the relevant user, existing workflow, available data, risk tolerance, and a definition of success.

In the second phase, the focus shifts to developing a minimum viable solution. The FDE builds Python services, normalizes data, logs events, and creates interfaces to the best of their ability. While an AI model may be part of the solution, it is merely one component within a larger system.

The third phase involves integrating the solution into the broader system. This entails addressing critical concerns like identity and access management, secrets management, retries, observability, retention policies, latency, billing, and rollback procedures. The FDE must ensure that the developed system effectively addresses the original problem rather than merely exploring a research question.

Finally, during the enablement phase, the FDE hands off the solution to the user base, providing documentation on known limitations, success criteria, and reporting procedures for incorrect outputs. Effective communication of the system's nuances, both technically and in terms of business value, is crucial at this stage.

When building AI solutions as an FDE, a practical framework can help guide the process. This framework consists of five questions: problem definition, interface design, constraints identification, evaluation methods, and rollout strategy. By adhering to these questions, developers can avoid conflating a polished demonstration with a fully functional production system.

Measuring success in an FDE project often requires a more comprehensive view than simply assessing the model's accuracy. Metrics such as retrieval accuracy, task-completion rate, human correction rate, response time, cost per request, and other relevant measures can provide a more holistic assessment of the solution's effectiveness.