The new Silicon Valley role FDE is gaining popularity. What kind of AI talent do companies need?

Title: Silicon Valley's New Role: FDE Gains Popularity — What Kind of AI Talent Do Companies Need?

Author: Rhythm BlockBeats

Source:

Reprint: Mars Finance

Editor's Note: As companies like OpenAI and Anthropic begin forming AI Forward Deployed Engineer (FDE) teams, an old role originating from Palantir is making a comeback in Silicon Valley. The core value of FDE is to go onsite at clients’ locations, transforming general large models into Agent workflows tailored to specific business processes.

But what this article truly discusses is not just the new profession of FDE, but how job structures in the AI era are reshaping. The author believes that compared to the small number of FDEs dispatched inside client organizations to serve specific vendor products, the greater future demand will be for in-house AI Engineers. They need to understand prompt engineering, Agent frameworks, evaluation systems, and also be proficient with AI programming tools like Claude Code and Codex, embedding AI capabilities into software and business systems.

This also means that AI’s impact on the job market may not be simply “replacement.” It’s more likely to first create a batch of new generalist roles, which will then evolve—similar to how software engineers diversified into front-end, back-end, mobile, and DevOps—into more specialized careers like LLMOps, Evals Engineer, AI Data Engineer, and others. The truly scarce talent will be those who understand both engineering implementation and business scenarios.

Below is the original text:

Recently, a new role in Silicon Valley has attracted considerable attention: AI Forward Deployed Engineer (FDE). These engineers are dispatched into client organizations to help customize solutions, such as building and tuning Agent workflows that meet specific client needs. Since OpenAI and Anthropic started forming new teams and deploying FDEs into client organizations, I’ve heard many people re-engage with this career path.

The rise of FDE roles driven by AI workloads is an example of AI creating new jobs. It also shows that the narrative of an impending “jobpocalypse” where the job market collapses is not valid—there will still be many AI-related and non-AI-related roles in the future. However, as explained below, I believe the number of AI Engineer roles will far surpass that of FDEs.

The FDE role was roughly pioneered about twenty years ago by Palantir. At that time, Palantir would send engineers to work onsite at government agencies, often in secure, isolated environments disconnected from external networks. Besides solid technical skills, FDEs needed communication skills and sometimes business judgment. For example, they might need to communicate with clients, understand client needs, prioritize projects, explain complex technology, and provide respectful but firm feedback when clients’ requests were unrealistic. Today, FDEs are gaining renewed attention mainly because embedding a ready-made large language model into enterprise workflows and customizing it to meet specific business needs requires substantial practical implementation work.

However, I believe the scale of AI Engineer roles will be much larger. A company might accept a small number of FDEs working internally, but most will want to involve more of their own staff in project development. For example, in my organization, we do hire FDEs, but the number of AI engineers we employ is much larger. Additionally, a common concern among clients is the difficulty of finding truly “vendor-neutral” FDEs. After all, the core task of an FDE is to deeply integrate a vendor’s product into the enterprise system. At this stage, it’s hard to predict which AI service will be the best choice a year from now, so “optionality” is very important—meaning companies want the flexibility to choose the most suitable vendor in the future. In contrast, if FDEs deeply bind the company’s workflows to a single vendor, it significantly reduces this flexibility.

Currently, I see market demand for AI engineers rising rapidly. These engineers can build applications using AI software components, such as LLM prompts, Agent frameworks, evaluation systems, and so on; they are also proficient with AI programming agents like Claude Code, Codex, Antigravity CLI, and OpenCode. As the role of AI Engineer matures, I expect it will further split into more specialized positions. Similar to how “software engineer” was a general role decades ago and later diversified into front-end, back-end, mobile, data engineering, DevOps, and other directions.

What specialized AI engineering roles will emerge in the future? I cannot say for sure. Perhaps AI FDEs, LLMOps engineers, evaluation engineers, AI data engineers, Harness engineers, and some new roles we haven’t named yet. But at least for now, many generalist AI engineers are already creating enormous value. Top-tier AI engineers are in high demand. As this field continues to mature over the next decade, I also look forward to more professional specialization within AI engineering, which will create even more new employment opportunities.