A Practical Roadmap to Modern AI Engineering

🫠 Kind of a hot take, but any seasoned AI engineer would agree. You don't need to cram machine learning and deep learning to become an AI engineer today. I've seen too many people get obsessed with the wrong things, spend months learning concepts they rarely apply, and then never actually move toward the applied side of AI. In today's world, AI engineering at most companies isn't about training models from scratch. Unless your company is hosting and training its own models, which I'd usually call ML engineering, most AI engineers are working with hosted models, whether open source or closed source. So a lot of AI engineering today is about learning how to: - Understand how Large Language Models work under the hood (Briefly, you don't have to learn how to design attention and build transformers from scratch) - Choose the right AI model for different real world problems - Build AI agents using tools, knowledge through RAG, and memory - Evaluate AI systems before they fail in production - Know when to use a single agent versus a multi agent architecture - Deploy AI applications that improve through AI Ops And honestly, the biggest thing is mastering system design, which is essentially all of the above, but contextualized to a real problem with clear thinking around tradeoffs. I published a simple beginner friendly video to help people understand what to learn, and how to think about learning it, if they want to build AI systems. It's not another recycled AI engineer roadmap. It's a mind map for people who want to build but are looking for a starting point, check it out! https://lnkd.in/dJCnxguA

This post is largely opinion-based and reflects the author's perspective on becoming an AI engineer. While many of the practical recommendations align with current industry trends, the broader claim that machine learning and deep learning are unnecessary is subjective.

Confirmed Accurate

• Many AI engineering roles today focus on integrating and deploying pre-trained foundation models rather than training models from scratch.
• Understanding how Large Language Models work, selecting appropriate models, building RAG systems, developing AI agents, evaluating AI applications, and deploying production AI systems are common responsibilities in modern AI engineering.
• System design and trade-off analysis are increasingly important skills for production AI applications.
• ML engineering and AI engineering often have different focuses, with ML engineers more commonly responsible for model training and optimization.

Not Fully Accurate / Opinion

• The statement that "you don't need to learn machine learning and deep learning to become an AI engineer" is an opinion rather than a universal fact. Many AI engineering roles benefit from a foundational understanding of these subjects, even if they don't require building models from scratch.
• The distinction between AI engineer and ML engineer varies across companies. Job titles and responsibilities differ widely, and many organizations expect overlap between the two roles.
• The learning roadmap presented reflects one practical path into AI engineering, but it is not the only valid approach.