视频简介
AI Engineer这个岗位正在快速分化：有人还停留在调用API、写Prompt、做Demo的阶段，而另一部分工程师已经在构建可上线、可扩展的生产级AI系统。本期公开课将带你看清这一差距背后的本质——AI时代真正稀缺的不是“会用AI”，而是具备完整工程能力的人。 在这场分享中，你将系统了解AI Engineer Bootcamp的核心训练逻辑：从LLM基础到RAG系统，从Agent架构到Multi-Agent协作，再到Memory System与Harness Engineering等关键模块，逐步搭建一套完整的AI产品能力体系。课程强调的不只是知识，而是通过多个阶段的项目与Lab实操，让你真正具备构建生产级AI系统的能力。 同时，公开课也会深入讲解当前企业对AI工程师的真实要求，包括系统稳定性、可扩展性、评估与监控等关键能力，以及为什么这些能力正在成为求职与晋升的分水岭。如果你希望从“会用AI”升级到“能交付AI产品”，这场公开课将为你提供清晰路径与实战方向。 The role of an AI Engineer is rapidly diverging. While some are still focused on prompt engineering, API calls, and building demos, others are already developing production-ready AI systems that are scalable, reliable, and deployable. This session explores the real difference between these two levels—highlighting that in the AI era, the key is not just using AI, but building robust systems with strong engineering foundations. In this lecture, you will gain a structured understanding of the AI Engineer Bootcamp approach. The program is designed to guide you through building a full production-level AI system, covering essential components such as LLM fundamentals, RAG systems, agent architecture, multi-agent collaboration, as well as advanced topics like Memory Systems and Harness Engineering. Each phase focuses on practical, hands-on learning through labs and projects, rather than theoretical knowledge. The session also breaks down real industry expectations for AI engineers, including system reliability, scalability, evaluation pipelines, and observability. You will understand why these engineering capabilities are becoming critical differentiators in hiring and career growth. If you want to move beyond simply “using AI” and start building real-world AI products, this session will give you a clear roadmap and practical direction to get there.