人工智能工程师:初学者的务实学习路线图

发布于 2026-01-24 00:36:38

今天,我想和大家聊聊如何成为一名人工智能工程师——但会采用务实的视角,充分考虑大多数人实际的起点。

市面上有无数“人工智能工程师学习路线图”,声称只需几个月就能让你成为人工智能工程师。作为一名在亚马逊(Amazon)从事应用机器学习工作、专注于机器学习基础设施及生成式人工智能内容理解的从业者,我可以负责任地告诉你:要在大型科技公司成为一名人工智能工程师,需要投入大量时间并付出持续的努力。

但好消息是,要开始构建实用的人工智能应用,你并不需要掌握所有那些高级技能!大多数自学成才的开发者并不会从大型科技公司起步;他们通常从个人项目做起,或加入初创公司,亦或是开发自己的人工智能应用,在实践过程中不断学习。

因此,下面我将拆解一份更务实的学习路线图,清晰说明在你学习旅程的每个阶段,真正需要掌握的内容。

[](https://link.juejin.cn?target=)什么是人工智能工程师

首先,我们需要明确人工智能工程师的具体工作内容,因为很多学习路线图甚至都没有准确描述这个岗位!它们往往描述的是数据科学或传统机器学习工程师的工作范畴。

人工智能工程师的核心职责并非像数据科学家或机器学习工程师那样从零开始训练模型。相反,人工智能工程师主要负责以下工作:

  • • 基于GPT-4、LLaMA等预训练基础模型构建应用程序
  • • 专注于通过提示工程、检索增强生成(Retrieval-Augmented Generation,RAG)和微调实现模型适配
  • • 优先考虑可扩展性、评估、推理优化及实际场景部署
  • • 处理端到端系统,包括安全性、数据处理和用户反馈

这是一个以软件工程技能为基础的专业岗位,根据个人背景不同,有多种入行途径。接下来,我们就来聊聊这一点。

[](https://link.juejin.cn?target=)基础技能

在深入学习人工智能特定技术之前,你需要掌握一些基础技能:

  • • 数学基础:统计学、概率论和基础线性代数有助于你理解技术背后的原理。你不需要拥有数学博士学位,但至少要从概念上理解矩阵运算、概率分布等概念。
  • • Python编程:即使是部署自己的人工智能应用,你也需要能够编写生产级别的代码。在该领域,扎实的Python技能几乎是必备条件。
  • • 基础软件开发概念:包括使用Git进行版本控制、命令行基础操作,以及理解如何使用API。你需要知道不同服务之间如何通信,以及如何有效管理代码。
  • • 机器学习基础概念:尽管人工智能工程师不常从零开始训练模型,但理解监督学习与无监督学习的区别、基础模型评估指标,以及过拟合、欠拟合等概念,能让你掌握理解更高级主题的“通用语言”。

这些基础至关重要,因为人工智能工程本质上是在软件工程学科基础上发展起来的专业领域。没有这些基础,你在学习更高级概念时会举步维艰。

[](https://link.juejin.cn?target=)构建你的第一个人工智能项目

掌握上述基础后,你就可以开始接触人工智能工具和模型了。首先,你需要完成以下步骤:

  1. 学习使用人工智能API:像OpenAI API这样的服务能让你无需自行构建模型,就能集成强大的模型功能。这是开始构建具备人工智能能力的实际应用最快的方式。
  2. 理解提示工程:学习如何与人工智能模型进行有效“沟通”是一项关键技能。设计精良的提示能显著提升模型输出质量,对于获得稳定结果至关重要。
  3. 构建简单的RAG应用:这需要通过向量数据库和嵌入技术,将人工智能模型与你自己的数据源连接起来,从而让模型能更有效地回答你的问题。
  4. 尝试不同的预训练模型:利用Hugging Face等平台获取他人开发的模型。这能让你在无需自行训练或微调模型的情况下,积累对不同模型架构的经验。
  5. 掌握基础应用架构:你需要开始学习如何构建人工智能应用的结构,包括恰当的输入处理、上下文构建和输出处理。

在这个阶段,你的重点是“应用”而非“创造”——即利用现有工具构建实用项目。大多数自学开发者都是从这个阶段起步的!你可以开发聊天机器人、内容生成器、简单分类系统等应用,解决实际问题。

如果你需要帮助来学习构建项目所需的全部技能,DataCamp平台提供了优质课程,助力你打下人工智能工程基础:

  • • 对于希望将人工智能集成到应用中的开发者,其“开发者人工智能工程师助理”(Associate AI Engineer for Developers)学习路径会教你如何使用OpenAI API、Hugging Face、LangChain,以及Pinecone等向量数据库。
  • • 对于拥有数据科学背景、希望深入学习基础模型应用的人,“数据科学家人工智能工程师助理”(Associate AI Engineer for Data Scientists)学习路径能帮助你向人工智能工程领域转型。
  • • 如果你是完全的初学者,“人工智能基础”(AI Fundamentals)学习路径会通过无代码方式,为你介绍人工智能核心概念。

这些课程的优势在于,它们能为你打下必要的基础,让你:

  • • 开始构建实际项目——这是学习过程中最重要的环节
  • • 发现人工智能工程领域中最吸引你的方向,以便继续你的学习旅程

[](https://link.juejin.cn?target=)迈向专业人工智能工程师

拥有扎实的基础并完成一些实际项目后,你自然会希望深化自己的知识。

此时,阅读奇普·胡恩(Chip Huyen)所著的《人工智能工程》(AI Engineering)是绝佳选择,该书涵盖了成为一名实战型人工智能工程师所需理解的全部内容。阅读这本书及其参考文献,能让你在学习道路上稳步前行。

顺便说一句——我在自己的YouTube频道上制作了一个76分钟的完整版总结视频,拆解了书中所有关键概念。你可以在阅读前观看该视频进行预习,也可以在阅读后观看以巩固复习。

随着你完成更多项目,并学习诸如奇普所著书籍等资料,你的技能会自然拓展,逐渐涵盖以下领域:

  • • 深度学习:理解神经网络的复杂细节、训练过程以及基础模型的工作原理。学习Transformer架构、注意力机制,以及嵌入技术如何捕捉语义信息。
  • • 部署与基础设施:学习使用Docker进行容器化,以及在AWS、GCP、Azure等平台上进行云部署。理解系统架构、监控和日志管理。
  • • 高级RAG技术:实现更复杂的文档分块策略,优化嵌入技术,并理解不同检索方法之间的权衡。
  • • 基础模型应用:掌握低秩适应(LoRA)等微调技术,并根据成本、性能和授权许可之间的权衡,做出明智的模型选择决策。
  • • 构建稳健的评估系统:建立系统化的模型性能测试方法,衡量幻觉现象与偏差,并实施自动化和人工评估流程。
  • • 推理优化:通过量化、蒸馏、优化部署架构等技术,提高模型运行速度和效率。
  • • 智能体系统:构建能够分解复杂任务、有效使用工具,并在长时间交互中保持上下文的人工智能应用。
  • • 安全、隐私与伦理:实施防范提示注入等攻击的防护措施,确保隐私合规,并考量人工智能系统的伦理影响。

在这个阶段,你正从“使用人工智能工具”向“理解工具工作原理并针对特定场景优化工具”转变。中级与高级人工智能工程师的区别,往往就在于是否精通这些高级主题。没有这些技能,你将难以构建具备规模化运营能力或能应对复杂实际场景的系统;而掌握这些技能后,你将有资格应聘顶级公司中从事前沿人工智能应用的岗位——这类岗位的薪资通常可达30万至40万美元。

在这个高级阶段,还有更多专业技能需要学习——从数据集工程到应用架构模式等。

[](https://link.juejin.cn?target=)务实的时间规划

从这份充满技术细节的清单中不难看出,想从完全的初学者在三个月内成长为专业人工智能工程师,是不现实的。以下是更务实的时间规划:

  • • 掌握基础并构建首个人工智能应用:约6个月(兼职学习)
    这一前提是你能持续学习,且已具备一定编程基础。如果从零基础开始,需额外增加6个月。
  • • 熟练掌握更高级概念:再需6至12个月
    在此期间,你应不断构建更复杂的项目,并深化对底层技术的理解。
  • • 达到专业水平:再需1至2年
    这一阶段,你将培养在大型公司或高级人工智能应用项目中任职所需的专业技能。
  • • 成为顶级公司的高级/主管级人工智能工程师(薪资真正达到30万美元以上) :再需3至5年
    要精通人工智能工程的全领域技能,并积累领导复杂项目的经验,需要在该领域投入大量时间。

总体而言,如果你从零基础开始,且以兼职方式学习,预计需要3至6年才能达成目标。

这看起来时间很长?确实如此,但也符合实际情况。

请记住,你完全可以更早地开始构建项目,甚至进入该领域工作——无需等到掌握所有技能才行动。

如果你想更深入地学习大模型,以下是一些非常有价值的学习资源,这些资源将帮助你从不同角度学习大模型,提升你的实践能力。

本文较长,建议点赞收藏。更多AI大模型应用开发学习视频及资料,在这里

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