What Does a Forward Deployed Engineer Do? 5 Key Roles

A Forward Deployed Engineer (FDE) is a technology professional who combines Software Engineering, customer engagement and hands-on implementation to solve real business problems. As companies increasingly move from experimenting with Artificial Intelligence (AI) to integrating it into everyday operations, the demand for engineers who can turn complex technology into practical solutions is growing rapidly.

Unlike traditional software engineers, who may spend most of their time developing products internally, Forward Deployed Engineers work closely with customers to understand their requirements, build or adapt solutions, deploy them within existing systems and ensure they deliver measurable results. Their responsibility extends beyond writing code: they must make sure the technology works in the customer’s actual business environment.

The role has gained attention across AI companies, enterprise software providers, software-as-a-service (SaaS) businesses and consulting firms. However, the definition of an FDE varies between employers, making it important for job seekers to understand what the position involves before applying.

According to job-search website Indeed, Forward Deployed Engineer job postings increased by 729% in April 2026 compared with April 2025, rising from 643 to more than 5,300. The increase highlights the growing interest in a role that combines technical expertise with practical problem-solving and customer collaboration.

The position is particularly relevant as organisations seek to deploy AI applications that work with their internal data, existing software and business processes. Building a promising demonstration is one thing; making the same system reliable, secure and useful in everyday operations is another.

Here are five key responsibilities that explain what a Forward Deployed Engineer does, which skills the role requires and how it differs from other technology jobs.

1. Define the Real Business Problem Before Building a Solution

The first responsibility of a Forward Deployed Engineer is to understand what the customer actually needs. Before writing code or selecting an AI model, an FDE works with customers and technical teams to examine existing processes, identify problems and determine what a successful solution should achieve.

This discovery stage is important because businesses do not always need new technology to solve their problems. Sometimes, an inefficient workflow, unclear responsibilities or poorly organised data is the real obstacle. Introducing AI without addressing these underlying issues may add complexity without delivering meaningful improvements.

An FDE must therefore ask practical questions: What is the organisation trying to accomplish? How is the task performed today? Which steps consume the most time? Where do errors occur? What information is available, and what restrictions apply to its use?

The answers help the engineer determine whether AI is appropriate, which technical approach might work and what limitations must be considered before development begins.

For example, a company might want to use AI to help employees find information across thousands of internal documents. Before building a chatbot, the FDE would need to understand how those documents are stored, who can access them, how frequently they change and what kinds of questions employees need answered.

The engineer would also work with the customer to establish measurable success criteria. These might include reducing the time required to find information, improving response accuracy or lowering the amount of repetitive manual work. The precise measurements would depend on the business problem rather than on the technology being used.

OpenAI’s public job description for FDE roles, for example, emphasises working closely with strategic customers to understand their business challenges and technical requirements.

This ability to translate business needs into technical requirements distinguishes an FDE from an engineer who receives a clearly defined development task and focuses primarily on implementing it.

Key skills required: Problem-solving, customer communication, requirements gathering, business-process analysis and the ability to translate ambiguous requirements into practical technical plans.

2. Build or Adapt an AI Solution for the Customer

Once the business problem is clearly defined, the Forward Deployed Engineer helps develop a solution that addresses it. This can involve writing application code, connecting existing software, adapting AI models or designing workflows that allow an AI system to perform useful tasks.

In many cases, the work involves building applications on top of large language models (LLMs). These models power conversational AI systems and can perform tasks such as summarising documents, extracting information, answering questions and generating text.

However, simply connecting an application to a language model is rarely enough for a production-ready business solution. The engineer must consider the customer’s data, workflow, security requirements and expectations for accuracy.

One common approach is retrieval-augmented generation, or RAG. This technique allows an AI application to retrieve relevant information from an external knowledge source, such as a company’s internal documents, before generating an answer.

RAG can help businesses create applications that respond using relevant company information instead of relying exclusively on knowledge acquired during a model’s training. The system must still be designed carefully to retrieve appropriate information, respect access permissions and reduce the risk of inaccurate responses.

Another possibility is an AI agent that can carry out a sequence of actions. Rather than simply answering a question, an agent might retrieve information, call an approved tool, update a record or pass a task to another system. The precise capabilities depend on the model, software integrations and permissions granted to it.

Standards and technologies such as Model Context Protocol (MCP) can help connect AI applications to external tools and data sources. An FDE may use these integrations to enable an AI system to interact with existing business software without rebuilding every underlying service.

Much of this work involves adapting existing models and infrastructure rather than training a new AI model from scratch. The challenge lies in combining the right components into a reliable application that meets the customer’s needs.

For instance, an organisation might want an AI assistant that helps its support team find relevant information and prepare responses. The FDE could connect an existing language model to an approved knowledge base, build the application interface, integrate it with the support workflow and introduce controls to reduce incorrect or unauthorised responses.

The engineer must also consider practical trade-offs involving performance, operating costs, response speed, reliability and maintainability. A solution that performs well in a demonstration may not be suitable for thousands of employees or customers without further engineering.

Key skills required: Software development, API integration, AI application development, familiarity with LLMs, data handling, system design and the ability to evaluate different technical approaches.

3. Deploy the Solution Inside the Customer’s Environment

Building an AI application is only part of the job. A Forward Deployed Engineer must also help make it work inside the customer’s existing technology environment.

Enterprise customers often operate complex systems that have evolved over many years. Their software may include legacy applications, internal databases, access-control systems and infrastructure that cannot easily be replaced. A new AI solution must work within these constraints rather than assuming that the customer can start from scratch.

Deployment also involves security and compliance requirements. The application may process confidential business records, customer information or other sensitive data. Before the system goes live, the customer’s IT and security teams may need to review its architecture, permissions, data flows and operating procedures.

The FDE must understand these requirements and adapt the implementation accordingly. This can involve configuring access controls, integrating authentication systems, limiting which information an AI application can retrieve and ensuring that the system follows the customer’s data-handling policies.

Testing is another major responsibility. AI applications can produce different responses to similar requests, and their output may be incorrect, incomplete or inconsistent. Engineers therefore need ways to evaluate performance beyond checking whether the application runs successfully.

One important technique is the use of evaluations, commonly called evals. These are structured tests designed to measure how well an AI system performs specific tasks. An evaluation might examine whether an application retrieves the correct information, follows instructions, respects access restrictions or produces acceptable responses to representative questions.

FDEs can use these tests to establish a baseline before deployment and check whether changes improve or degrade performance. Repeating evaluations after updates helps teams identify regressions before they affect customers.

Testing does not eliminate every possible failure, particularly when AI systems encounter unfamiliar inputs or unusual circumstances. However, systematic evaluation provides a more reliable basis for deployment decisions than relying on a handful of successful demonstrations.

The engineer may also need to troubleshoot integration problems, improve response times, monitor errors and coordinate with infrastructure teams. These tasks can require patience because the obstacles are not always related to the AI model itself. They may arise from data quality, network restrictions, outdated software or organisational processes.

This is where the FDE’s combination of engineering and customer engagement becomes particularly valuable. The engineer must understand the technical issue while also helping different teams agree on a workable solution.

Key skills required: System integration, software testing, AI evaluation, cybersecurity awareness, deployment practices, debugging and collaboration with IT and security teams.

4. Explain Technical Work to Non-Technical Stakeholders

A Forward Deployed Engineer must be able to explain technical decisions to people who may not have an engineering background. Customers often include business leaders, department managers and operational teams who need to understand what a solution can do and how it affects their work.

These stakeholders may not be interested in the details of a model architecture or software integration. They need clear answers to practical questions: What problem will the system solve? How reliable is it? What information can it access? What are its limitations? When will it be ready, and what could delay deployment?

The FDE must translate technical complexity into information that customers can use to make decisions. This does not mean avoiding difficult conversations. It means explaining the issue in a way that is accurate, understandable and relevant to the customer’s priorities.

For example, if an AI assistant sometimes produces incorrect answers, the engineer should explain the conditions under which errors occur, the steps being taken to reduce them and the safeguards that will be required before the system can be used more widely.

Similarly, if a deployment takes longer than expected because the customer’s internal systems require additional integration work, the FDE needs to explain the reason for the delay and what must happen next.

Communication becomes particularly important when expectations are unrealistic. A customer may assume that AI can automate an entire workflow immediately, while the engineer understands that some steps still require human review or additional testing.

Setting realistic expectations helps prevent misunderstandings and supports a stronger working relationship. It also allows the customer to make informed decisions about how the technology should be introduced and what level of human oversight is appropriate.

FDEs often act as a link between business stakeholders and engineering teams. They may explain customer requirements to internal developers, communicate technical constraints to decision-makers and coordinate feedback across both sides.

Trust is essential in this relationship. Customers are more likely to adopt a new system when they understand how it works, what it can and cannot do, and how problems will be handled.

Key skills required: Clear communication, presentation skills, expectation management, stakeholder coordination, active listening and the ability to explain technical concepts without unnecessary jargon.

5. Support the Customer After Launch Until the Solution Is Used Daily

The work of a Forward Deployed Engineer does not necessarily end when an application goes live. Deployment is an important milestone, but the solution must also become a useful and dependable part of the customer’s daily operations.

After launch, an FDE may work closely with users to identify problems, answer questions, resolve bugs and improve the system based on real-world feedback. The engineer must understand how the application behaves outside a controlled testing environment and determine whether it delivers the expected business value.

Some issues may only become visible after employees begin using the system regularly. Users might ask questions that were not included in the original evaluation tests, discover that an integration does not support an important workflow or report that the application is slower than expected during busy periods.

The FDE must investigate these problems and decide whether the solution requires a software change, better data, revised instructions, additional safeguards or adjustments to the customer’s workflow.

Training is another important responsibility. Employees need to understand how to use the application correctly, recognise its limitations and know when a task should be escalated to a human expert.

Over time, the engineer should help the customer’s own technical team develop the knowledge required to operate and maintain the system. Depending on the project, this may involve documentation, training sessions, troubleshooting procedures and monitoring tools.

The goal is to make the solution sustainable without requiring the FDE to remain permanently involved in every routine decision or technical issue.

This is one of the key differences between a Forward Deployed Engineer and a traditional consultant. A consultant may be responsible for analysing a problem, recommending an approach and delivering a report or implementation plan. An FDE is typically more directly involved in building and deploying the solution and helping ensure that it works in practice.

The distinction is not absolute because consulting roles vary, and some consultants also take responsibility for implementation. However, the FDE role places particular emphasis on hands-on engineering and the operational success of the deployed technology.

The experience gained from one deployment can also benefit other customers. Anthropic’s FDE job listing, for example, emphasises identifying repeatable deployment patterns and sharing insights with internal product teams.

When engineers encounter similar integration challenges across multiple organisations, they can help identify opportunities to improve the core product, simplify deployment processes or develop reusable technical components.

Key skills required: Troubleshooting, user training, production support, documentation, continuous improvement and the ability to transfer technical knowledge to customer teams.

How Is a Forward Deployed Engineer Different From a Software Engineer?

Although both roles require engineering expertise, their day-to-day responsibilities can differ significantly.

A traditional software engineer generally focuses on designing, developing, testing and maintaining software products or internal systems. The work may involve collaborating with product managers, designers and other engineers to deliver features against a defined roadmap.

A Forward Deployed Engineer also writes code and solves technical problems, but the work is more closely connected to an individual customer’s business environment. The engineer must understand customer requirements, adapt solutions to existing systems and help ensure that the technology is successfully adopted.

The distinction is especially relevant in enterprise AI, where a technically impressive model may not deliver value unless it works with a customer’s data, software, security policies and everyday processes.

For example, a software engineer might develop a reusable document-search feature for a product. An FDE working with a customer may need to configure that feature for the organisation’s data, connect it to existing applications, test access permissions and help employees use it effectively.

The two roles can overlap, and the precise division of responsibilities depends on the employer. Some FDE positions involve substantial coding, while others include more architecture, implementation planning and customer coordination.

Forward Deployed Engineer vs Solutions Engineer and Sales Engineer

Forward Deployed Engineers are also sometimes confused with solutions engineers and sales engineers because all three roles involve working with customers and explaining technology.

A sales engineer typically supports the sales process by demonstrating how a product works, answering technical questions, assessing customer requirements and helping prospective buyers understand whether a solution meets their needs.

A solutions engineer may work across pre-sales and implementation activities, depending on the organisation. The role often focuses on designing a suitable technical solution, demonstrating integrations and helping customers understand how different components can work together.

A Forward Deployed Engineer is generally more directly involved in engineering and implementation within the customer’s environment. The role may include writing application code, adapting AI systems, integrating internal software, running evaluations and resolving technical problems after deployment.

These distinctions are not universal. Some companies use the titles interchangeably, while others assign different responsibilities to each position. Job seekers should therefore examine the actual duties, expected coding workload, customer-facing requirements and post-deployment responsibilities listed in each job description.

What Skills Do You Need to Become a Forward Deployed Engineer?

Becoming a Forward Deployed Engineer requires a combination of technical depth and the ability to work effectively with customers. Strong programming skills are important, but they are only one part of the profile.

• Software engineering: Knowledge of programming, APIs, debugging, system design and software development practices helps engineers build and maintain working applications.
• AI and machine learning fundamentals: Familiarity with large language models, model limitations, prompting, retrieval-augmented generation and AI evaluation can be valuable for AI-focused FDE positions.
• Data and integration skills: Experience with databases, data pipelines, authentication and enterprise software integrations helps engineers connect new applications to existing systems.
• Deployment and security: Understanding testing, monitoring, access controls, cloud infrastructure and production operations is useful when moving applications into real customer environments.
• Customer communication: Engineers must be able to gather requirements, explain trade-offs, manage expectations and collaborate with people who have different levels of technical knowledge.
• Adaptability: Customer problems are often ambiguous. FDEs need to investigate unfamiliar systems, prioritise competing requirements and adjust their approach when assumptions prove incorrect.
• Business understanding: The ability to connect engineering decisions to measurable outcomes helps ensure that the solution addresses a genuine business need rather than simply demonstrating technical capabilities.

Not every position requires the same combination of skills. An FDE working on AI infrastructure may need deeper knowledge of deployment systems and model performance, while one focused on business applications may spend more time integrating enterprise software and understanding operational workflows.

Applicants should therefore review each employer’s expectations rather than assuming that all Forward Deployed Engineer positions have an identical technical profile.

What Does a Forward Deployed Engineer’s Typical Workweek Look Like?

The FDE role can surprise engineers who expect to spend nearly all their working hours writing code. Customer meetings, requirements discussions, deployment planning and coordination with internal teams can occupy a substantial portion of the week.

One 2026 survey of approximately 1,500 Forward Deployed Engineers cited in the supplied report found that a median workweek was divided into roughly 47% customer-facing activities, 31% writing or reviewing code and 22% internal coordination.

These figures illustrate the role’s hybrid nature, although the exact balance can vary considerably by employer, project stage and customer requirements.

During the discovery phase, an FDE may spend more time interviewing users, mapping workflows and documenting requirements. During implementation, coding and integration work may become more prominent. After deployment, the focus may shift towards testing, troubleshooting, training and improving adoption.

The work can also involve switching frequently between different types of problems. An engineer might investigate a technical bug in the morning, explain a deployment delay to a customer in the afternoon and coordinate with an internal product team later in the day.

For people who enjoy understanding business problems and working directly with customers, this variety can be attractive. Engineers who prefer long periods of uninterrupted coding may find the frequent communication and coordination more demanding.

Neither preference is inherently better. Traditional software engineering and forward deployment offer different ways to build a technology career, and the right choice depends on an individual’s strengths and preferred working style.

Why Forward Deployed Engineers Are in Demand in the AI Industry

The growing interest in FDEs is closely connected to the difficulty of deploying AI in real business environments. Organisations can access increasingly capable models, but turning those models into reliable applications often requires substantial engineering and integration work.

A business may have access to an advanced language model but still need help connecting it to internal data, restricting access to confidential information, evaluating the quality of its answers and integrating the application into existing workflows.

These tasks require more than model selection. They involve understanding the customer’s operations, designing appropriate software, managing deployment risks and improving the application as people begin using it.

Forward Deployed Engineers can help bridge the gap between AI capabilities and business outcomes. They work close enough to customers to identify practical problems while possessing the engineering skills needed to implement solutions.

The role can also create a feedback loop between customers and product developers. Patterns discovered during deployments may reveal common requirements, recurring technical obstacles or product features that would benefit a wider group of customers.

That information can help companies improve their products rather than solving the same problem separately for every organisation.

However, the growth in job postings does not mean every business will need a large FDE team or that demand will increase at the same rate indefinitely. Hiring will depend on companies’ AI investment plans, their deployment models and the extent to which products become easier to configure without specialised engineering support.

Is a Forward Deployed Engineer Career Right for You?

A Forward Deployed Engineer career may suit people who enjoy both building software and understanding how businesses operate. It can be particularly appealing to engineers who like solving open-ended problems, communicating with customers and taking responsibility for whether a technical solution works in practice.

The role can offer exposure to several parts of the technology-development process, including requirements gathering, software architecture, implementation, testing, deployment and product improvement. It may also help engineers develop a stronger understanding of how technical decisions affect business outcomes.

However, the position comes with trade-offs. Customer deadlines, changing requirements and unfamiliar technical environments can make the work unpredictable. Engineers may need to explain delays, manage conflicting expectations and coordinate with several teams before a solution can be deployed.

People who prefer to concentrate primarily on software architecture, algorithm development or uninterrupted coding may find a more traditional engineering role better suited to their interests.

Before applying, candidates should examine the job description carefully. They should look for details about how much time the role involves coding, whether the work is customer-facing, how deployment responsibilities are divided and whether the engineer is expected to support systems after launch.

It is also worth checking how the employer defines success. Some organisations may emphasise customer adoption and implementation outcomes, while others may place greater weight on technical delivery, reusable product improvements or revenue-related goals.

The Bottom Line: An FDE Makes Technology Work in the Real World

A Forward Deployed Engineer combines software development, business understanding and customer collaboration to turn technical capabilities into practical solutions. The role covers five major responsibilities: defining the real problem, building or adapting the solution, deploying it within the customer’s environment, explaining technical decisions and supporting adoption after launch.

As AI becomes more widely integrated into enterprise operations, these responsibilities are gaining importance. Companies need engineers who can do more than demonstrate what a model can achieve; they need people who can make AI applications reliable, secure and useful within existing business systems.

The role is not simply a new title for a traditional software engineer, consultant or sales engineer. Although it overlaps with all three, its defining feature is the combination of hands-on engineering and direct responsibility for implementation in the customer’s environment.

For engineers who enjoy coding but also want to work closely with customers, the FDE role offers a career path that connects technical development with real-world problem-solving. For those who prefer to focus primarily on code, traditional engineering roles remain equally valid alternatives.

Ultimately, the value of a Forward Deployed Engineer lies in ensuring that technology delivers results beyond a prototype or demonstration. The job is complete not merely when the code works, but when the solution addresses a genuine need and becomes a dependable part of the customer’s daily operations.