Forward Deployed Engineer: what it is and how this model takes AI from pilot to production.

Many companies have already tested generative AI. They've done a proof of concept, built an internal chatbot, and presented a demonstration to management. Few have managed to put these initiatives into operation every day, integrated into their systems, with reliable data and measurable returns.

This gap between pilot development and production has gained a name in the technology market: it's the space where the Forward Deployed Engineer , or FDE, works. Companies like Palantir, OpenAI, and Anthropic use this model to bring their AI products into large organizations. In this article, we explain what this professional does, why they have become central to AI projects, and how your company can adopt this work model without having to build a team from scratch.

What is a Forward Deployed Engineer?

A Forward Deployed Engineer is a software engineer who works directly with the client to understand the problem, build the solution, integrate it with existing systems, and put everything into production. The term comes from military vocabulary: forward deployed refers to someone positioned on the front line.

In practice, FDE brings together skills that are normally scattered across various areas:

• Software Engineering: Writes code, creates APIs, integrations, and tests.
• Solutions engineering: Understands the client's architecture and chooses the right technologies.
• Consultancy: It maps business processes, constraints, and objectives.
• Product: Identifies recurring patterns among customers that can become a standard feature.
• Customer success: The work is only considered complete when the client uses the solution and sees results.

What sets FDE apart is its accountability for results. It doesn't deliver a report or a pretty demonstration. It delivers a working application.

Where did the FDE model come from?

The function became well-known at Palantir, which served governments, armed forces, and large corporations with very specific problems. The data was scattered, each organization had its own processes, and the security requirements were high. Sending software with a manual wasn't enough.

The response was to send engineers to work where the problem was occurring. They observed the operation, talked to users, analyzed the data, and built the solution together with the client.

With the arrival of language models, the model has made a strong comeback. OpenAI and Anthropic have created teams dedicated to this function because they realized something simple: making a powerful model available via API is only the beginning. The hard work begins when the company needs to connect this model to its data, systems, internal rules, and security requirements.

Why do so many AI projects stop at the pilot stage?

A typical client request rarely arrives as a technical specification. It usually comes like this: "we need to reduce the analysis of these documents from three days to a few hours." From that sentence, someone needs to figure out which documents are involved, where the data is located, which regulatory rules apply, which model to use, how to measure the quality of the responses, and how to safely deploy everything into production.

When that person doesn't exist, the project usually stalls at one of these points:

• The wrong problem is solved. The area calls for a chatbot, when the real problem is that nobody can find the right information. Or it calls for an autonomous agent for a process that still lacks clear rules.
• Proof of concept doesn't communicate with systems. It works with a test spreadsheet, but it doesn't connect to the ERP, CRM, or data lake.
• There is a lack of governance. Each area creates its own automation, without data usage policies or access control. This is what we call... AI without governance.
• No one measures the return. Without a defined indicator before the project, it becomes impossible to prove to the CFO that the investment was worthwhile.

The FDE exists to overcome these four obstacles. It asks the right questions, separates the real need from the solution suggested too early, builds, integrates, and monitors its use.

What does a FDE do on a daily basis?

The routine varies depending on the client, but a single week might include meetings with executives, data architecture analysis, a Python API, a React interface, cloud resource configuration, testing of a RAG pipeline, and troubleshooting an AI agent.

There is also a less visible but very important task: deciding what should be custom-made and what should become standard. Four questions guide this decision:

1. What needs to be built specifically for this client?
2. What should go to the main platform and serve everyone?
3. What can be resolved through configuration alone?
4. What would create a technical debt that is impossible to maintain?

This judgment is as valuable as knowing how to program. It's what prevents the company from accumulating dozens of isolated solutions that no one can sustain.

FDE, consultant and developer: what's the difference?

	Traditional consultant	Assigned developer	Forward Deployed Engineer
Main delivery	Diagnosis and recommendation	Functionality specified by third parties	Solution working in production.
Starting point	Closed scope	Requirements ready	The business problem is still vague.
Do you write code?	Rarely	Yes	Yes, from start to finish.
How is it measured?	Report quality	Deadline and completed tasks	Real-world use and business impact

How to implement the FDE model without hiring a team from scratch.

The challenge for most Brazilian companies is that this profile is rare and expensive. Finding someone who is proficient in software engineering, cloud computing, data management, generative AI, and also communicates well with management takes months. Maintaining an entire team with this profile costs even more.

To solve this, Flexa Cloud created the Center of Excellence (CoE) in Hyperautomation and Governed AI . In practice, it's the Forward Deployed model applied as a service: a team of AWS specialists acting as an extension of your company, close to the problem and responsible for the result, under an annual contract with predictable costs.

The CoE follows a six-step methodology, the Flexa Pipeline , which organizes all AI and automation requests into a single queue:

1. Intake: Capturing the pains and ideas from all areas.
2. Classificação: initial technical and strategic screening.
3. Analyze: Feasibility study and solution design.
4. Prioritization: Ranking based on expected return and technical effort.
5. Execution: Agile development and native deployment on AWS.
6. Measuring ROI: Verification of the real impact using the Business Value Realization (BVR) methodology.

Beyond the engineering team, the CoE includes governance and AI usage policies for the entire organization, centralized demand management, fine-tuning of models for specific cases, and workshops to prepare the internal team. If you want to understand the concept in more detail, also read why companies are adopting the AI ​​Center of Excellence.

Results from those who brought AI to production.

The work model of focusing on the problem itself, with accountability for the outcome, has already generated concrete results for Flexa's clients:

• OLX: The posting of advertisements was left until 3 times faster with AI-powered autofill, used by approximately 250 people per day.
• Gimba: The registration time for each product has decreased. 84 %From 13 to 2 minutes, using generative AI on Amazon Bedrock.
• Britannia: The virtual assistant Tânia responds between 30% and 40% of calls Internal IT support, integrated with the ticketing system.

Details of two of these projects are in the article Generative AI in practice: efficiency lessons from OLX and Gimba.

Does your company need a Forward Deployed model?

This model is worth considering if you recognize yourself in at least two of these situations:

• They've already done AI proof-of-concept tests, but none are in production.
• Each department is creating its own automations, without coordination from IT.
• The backlog of automation requests just keeps growing.
• The board is asking what the return on investment in AI has been, and nobody has the number.
• Your internal team understands the business, but lacks experience with AI architecture on AWS.

Frequently asked questions about Forward Deployed Engineer

What does a Forward Deployed Engineer do?

He works closely with the client to understand a business problem, build the solution, integrate it with existing systems, and put it into production, being responsible for the final result.

What is the difference between FDE and a consultant?

The consultant typically delivers a diagnosis and recommendations. FDE writes the code, integrates the systems, and only considers the work complete when the solution is in use and generating impact.

Why is the FDE model important in AI projects?

Because the AI ​​model alone doesn't solve the problem. The value appears when it's connected to the company's data, systems, rules, and security requirements, and that's exactly what FDE does.

How can I implement the Forward Deployed model without hiring an in-house team?

With an AI-as-a-service Center of Excellence, such as Flexa Cloud's CoE , a senior team of AWS specialists works within your operation, employing a methodology for prioritization and ROI measurement, under an annual contract.