AI前沿部署工程师崛起
人工智能无法完全取代人类智慧，我在28年前于东京大学学报发表的文章中就已阐述过这一观点。
ChatGPT引发的焦虑 重温25年前人工智能初现光芒的亲身体验
今年3月13日，我在与友人交谈时预判，美国西海岸的大模型企业必将大规模挺进纽约，因为他们的客户集中在华尔街。
关于大模型、AI生态系统及其对资本市场的影响，可参阅我以下的文章和视频内容。
大模型投资收益分析
昨日吴恩达在其最新一期Newsletter《The Batch》中介绍了当前最炙手可热的职位：AI Forward Deployed Engineer（AI前沿部署工程师，简称FDE）。有咨询行业经验的朋友，是否觉得这个岗位与顾问（consultant）颇为相似？只不过SAP或Java开发框架等工具换成了Claude、GPT等大模型，对业务的深度洞察和理解同样不可或缺。
2026年4月日本大学毕业生就业率达到98%，创下历史新高。而美国大学毕业生却正经历就业困难的严峻考验，ChatGPT问世前火爆的计算机专业也遭受重大冲击。结合近期与朋友们的交流，我对美国就业困境的几个成因进行了思考，欢迎大家共同探讨：
1）AI对部分白领工作的替代，如编程、文档处理等
2）企业方（招聘方）对AI的学习和认知曲线，尚未形成AI应用和人才培养战略
3）科技公司在疫情期间过度招聘，目前仍在裁员调整中
4）高校人才培养的错配，顺应时代的改革势在必行。例如：纽约周边理工科强校的我校即将设立人工智能学院，全面拥抱AI。
以下是吴恩达文章的中文翻译及英文原文，版权归属于原作者。
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亲爱的朋友：
硅谷最近出现了一个备受瞩目的新职位：AI Forward Deployed Engineer（AI前沿部署工程师，简称FDE）。这类工程师会被派驻到客户组织内部，协助客户定制解决方案，比如构建和优化适合客户特定需求的智能体工作流（agentic workflows）。自从OpenAI和Anthropic开始组建新团队，将FDE安排到客户组织中之后，我听到不少人重新开始关注FDE这条职业发展路径。
AI工作负载中FDE的兴起，是AI正在创造新工作的一个例证，也说明"AI即将导致就业市场崩溃"的叙事是错误的。不过，我认为未来AI Engineer（AI工程师）的岗位数量会远远更多，下面我会解释原因。
FDE这个角色大约在二十年前由Palantir开创。当时Palantir会把工程师派到政府机构现场，在安全的、与外部网络隔离的系统中工作。除了扎实的技术能力，FDE还需要沟通能力，有时也需要商业能力。例如，他们可能需要和客户沟通，理解客户需求；制定策略，确定项目优先级；解释复杂技术；并且在客户提出不现实要求时，礼貌而坚定地提出反对意见。如今FDE重新受到关注，是因为把一个现成的LLM改造成适合某个企业具体业务需求的定制化智能体工作流，需要大量工作。
不过，我认为AI Engineer的岗位数量会大得多。一家公司可能会接受少数几名FDE嵌入自己的组织，但大多数公司会希望有更多自己的员工来参与项目。我的组织确实也会招聘FDE，但我们招聘的AI Engineer要多得多！此外，客户常见的一个担忧是：很难找到真正"供应商中立"的FDE。毕竟，FDE的任务就是把某个特定供应商的产品深度整合进公司系统。在现在这个很难预测一年后哪种AI服务会成为最佳选择的阶段，选择空间非常有价值，也就是未来可以自由选择最适合供应商的能力。相比之下，如果让FDE把公司的流程和某个供应商深度绑定，就会显著降低这种选择空间。
现在，我看到市场对AI Engineer的需求正在快速增长。这类工程师能够使用AI软件组件来构建软件应用，比如LLM prompts、智能体框架、评估系统（evals）等；同时也能高效使用AI编程智能体，比如Claude Code、Codex、Antigravity CLI和OpenCode。随着AI Engineer这个角色逐渐成熟，我预计它会进一步分化成更专业的岗位，就像几十年前通用的软件工程师角色后来逐渐分化为前端、后端、移动端、数据工程、DevOps等方向一样。
未来会出现哪些更细分的AI工程岗位？我还不知道。也许会有AI FDE、LLMOps工程师、Evals工程师、AI数据工程师、Harness工程师，以及其他我们现在还没有命名的角色。但就目前而言，我看到很多通才型AI Engineer正在创造大量价值。优秀的AI Engineer现在非常抢手！随着我们这个领域在未来十年继续成熟，我期待AI Engineering内部出现新的细分方向，并创造更多就业机会。
继续创造！
Andrew
Dear friends,
One of the new, buzzy jobs in Silicon Valley is the AI Forward Deployed Engineer (FDE), an engineer who is embedded within a client organization to help customize solutions, such as building and tuning agentic workflows that suit the client's particular needs. I've heard from people who are wondering anew about the FDE career path since OpenAI and Anthropic started building new teams to place FDEs within client organizations.
The rise of FDEs for AI workloads is one way AI is creating new jobs (and why the jobpolcalyse narrative of upcoming job market collapse is false). However, I believe there will be far more AI Engineer jobs, as I explain below.
The FDE role was pioneered about two decades ago by Palantir, which sent engineers to government locations to work on secure, air-gapped networks. In addition to having good technical skills, FDEs need communication skills and sometimes business skills. For example, they may need to speak with clients to understand their needs, formulate a strategy to prioritize projects, explain complex technology, and respectfully push back if a client asks for something unrealistic. They're enjoying a resurgence because of the amount of work involved in taking an off-the-shelf LLM and building it into a custom agentic workflow that fits particular business needs.
However, I believe the number of AI Engineer jobs will be far larger. A company might accept a few FDEs to be embedded within its organization. But most companies will want far more of their own employees working on their projects. While my organizations do hire FDEs, we hire far more AI Engineers! Also, a common client concern is that it is hard to find vendor-neutral FDEs — they are, after all, there to deeply integrate a particular vendor's product into a company. In this moment when it's hard to predict which AI service will be the best one in a year's time, optionality (the ability to pick whatever vendor turns out to fit best in the future)is very valuable. In contrast, letting FDEs tightly bind a company's processes significantly reduces optionality.
Right now, I see surging demand for AI Engineers who can build software applications using AI software components (like LLM prompts, agentic frameworks, evals, etc.) and effectively use AI coding agents (like Claude Code, Codex, Antigravity CLI, and OpenCode). As the AI Engineer role matures, I expect it to fragment into more specialized roles, like the generic Software Engineer role from decades ago fragmented into frontend, backend, mobile, data engineering, devops, and so on.
What will be the future, specialized AI engineering roles? I don't know. Perhaps there will be AI FDEs, LLMOps Engineers, Evals Engineers, AI Data Engineers, Harness Engineers, and other roles we don't have names for yet. But for now, I see a lot of AI engineers who are generalists createa lot of value. Skilled AI Engineers are in very high demand! As our field continues to mature over the coming decade, I look forward to new specializations within AI Engineering that create even more job opportunities.
Keep building!
Andrew
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