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AI Engineering
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AI engineering involves building software systems that use machine learning models and large language models (LLMs) as components. This includes selecting models, integrating them via APIs, engineering prompts, managing context, and deploying AI features in production. AI engineering is at the core of the job of FDEs. It's what turns an AI model into a product that runs reliably inside a company's infrastructure.
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Visit the following resources to learn more:
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- [@article@What Is an AI Engineer? (And How to Become One)](https://www.coursera.org/articles/ai-engineer)
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- [@video@AI, Machine Learning, Deep Learning and Generative AI Explained](https://www.youtube.com/watch?v=qYNweeDHiyU)
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# Backend
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Backend development refers to the server-side logic of a web application, including handling requests, running business logic, interacting with databases, and returning responses. Backend systems are typically built with Python, Node.js, Java, or Go, connected to databases and external services. FDEs who can build solid backend systems can own the full delivery of a feature rather than handing off at the API boundary.
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Visit the following resources to learn more:
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- [@article@What is backend? A comprehensive intro to server-side development](https://alokai.com/blog/what-is-backend)
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- [@video@How The Backend Works](https://www.youtube.com/watch?v=4r6WdaY3SOA)
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# Communication
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If you cannot explain what AI can and cannot do to a non-technical VP, you cannot be an FDE. Communication in this role is about translating between two different worlds: the technical reality of what you are building and the business reality of what the customer needs to justify the investment. That means being able to speak about token costs and latency in the same conversation where you are explaining ROI to an executive. It also means knowing when to say AI is not the right answer.
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# DevOps Skills
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DevOps is the practice of automating and streamlining how software is built, tested, and deployed. A forward-deployed engineer who knows DevOps can own the full deployment lifecycle, from spinning up infrastructure to shipping a containerized agent into a client's cloud environment, without depending on another team to get there.
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Visit the following resources to learn more:
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- [@article@What Is DevOps? A Guide to the Basics](https://www.coursera.org/articles/what-is-devops)
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- [@video@DevOps In 5 Minutes](https://www.youtube.com/watch?v=Xrgk023l4lI)
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# DSA & System Design
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Data Structures & Algorithms is the study of how to organize and process data efficiently. System Design is the practice of architecting large-scale, reliable systems. Together, they give FDEs the tools to make the right trade-offs when it comes to choosing the right data structure for an agent's memory layer, designing APIs that won't break under enterprise load, or architecting a multi-agent pipeline that scales across an organization.
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Visit the following resources to learn more:
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- [@article@Data Structures and Algorithms (DSA) Tutorial](https://www.tutorialspoint.com/data_structures_algorithms/index.htm)
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- [@article@AI System Design: A Complete Guide](https://www.systemdesignhandbook.com/guides/ai-system-design/)
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- [@article@System Design: Complete Guide](https://swimm.io/learn/system-design/system-design-complete-guide-with-patterns-examples-and-techniques)
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- [@video@What Are Data Structures?](https://www.youtube.com/watch?v=bum_19loj9A)
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# From X to FDE
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Three backgrounds tend to transition well into the FDE role: software engineers, consultants, and product managers. Software engineers already have the technical foundation but often need to develop the ability to communicate AI tradeoffs to non-technical stakeholders and build a portfolio that shows they can own a full deployment, not just write code. Consultants and PMs can already translate data into business outcomes, which is half the job, but need to close the gap on engineering by building real agents, RAG pipelines, and eval frameworks from scratch.
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Visit the following resources to learn more:
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- [@article@The Definitive Guide to Forward Deployed Engineer Interviews in 2026](https://www.sundeepteki.org/advice/the-definitive-guide-to-forward-deployed-engineer-interviews-in-2026)
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# Frontend
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Frontend development refers to the part of web development concerned with what users see and interact with in a browser. It covers HTML for structure, CSS for styling, and JavaScript for interactivity. FDEs who can build functional frontends are more self-sufficient in customer engagements, able to deliver end-to-end demos and working prototypes without depending on a separate frontend team.
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Visit the following resources to learn more:
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- [@book@Frontend Development Handbook](https://github.com/FrontendMasters/front-end-handbook-2019/blob/master/exports/Front-end%20Developer%20Handbook%202019.pdf)
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- [@video@Frontend web development - a complete overview](https://www.youtube.com/watch?v=WG5ikvJ2TKA)
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# Introduction
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A Forward Deployed Engineer is a software engineer who works directly inside a customer's environment to build, deploy, and stabilize AI systems. The role originated at Palantir, where engineers called Deltas would embed with clients, sometimes on military bases, to ship code overnight based on feedback from the field that same day. That same idea is now at the center of how companies like OpenAI and Anthropic are bringing AI into large enterprises. The job requires technical depth, the ability to read an unfamiliar codebase quickly, and the communication skills to explain what AI can and cannot do to a non-technical decision maker.
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Visit the following resources to learn more:
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- [@article@orward deployed engineer is AI’s hottest job](https://thenewstack.io/forward-deployed-engineer-fde-openai-google/)
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- [@article@Forward-deployed engineer: The complete guide](https://www.rocketlane.com/blogs/forward-deployed-engineer)
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# Linux
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Linux is an open-source operating system used widely in servers, cloud environments, and developer workstations. Most production software runs on Linux, so being comfortable navigating a Linux system, managing processes, reading logs, and configuring services is a practical necessity for anyone building and deploying in real customer environments.
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Visit the following resources to learn more:
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- [@course@Linux for Noobs](https://labex.io/courses/linux-for-noobs)
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- [@article@Practice Linux Fundamentals](https://labex.io/linuxjourney)
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- [@video@Linux Fundamentals](ttps://www.youtube.com/watch?v=kPylihJRG70)
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# Requirements Gathering
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Project requirements come from observation as much as from conversation. You watch how teams actually work, not just how they describe their work, because those two things are often different. The goal is to surface the undocumented workflow, the data source people actually trust, and the edge cases that would break an agent in its first week of production. Written requirements are useful, but the real requirements live in the room with the people doing the work.
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Visit the following resources to learn more:
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- [@course@Requirements Gathering in Business Analysis](https://www.coursera.org/learn/requirements-gathering-in-business-analysis)
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- [@article@Requirements Gathering in Software Engineering](https://www.jamasoftware.com/requirements-management-guide/requirements-gathering-and-management-processes/what-is-requirements-gathering/)
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- [@video@Requirement Gathering Techniques For A Business Analyst](https://www.youtube.com/watch?v=8EBWxW5Cn1g)
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- [@course@Requirements Gathering in Business Analysis](https://www.coursera.org/learn/requirements-gathering-in-business-analysis)
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- [@video@Requirement Gathering Techniques For A Business Analyst](https://www.youtube.com/watch?v=8EBWxW5Cn1g)

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