How NOT to Become an AI Engineer

Seven common mistakes people make when trying to break into AI engineering, and how to avoid them. The key distinction is that AI engineers build applications on top of pre-trained models rather than doing ML research. Mistakes include: over-investing in math/theory before writing application code, skipping software engineering fundamentals like Git and clean code, getting stuck in tutorial hell instead of building, memorizing tool APIs instead of understanding underlying concepts, trying to learn too many things simultaneously, never deploying projects beyond localhost, and building generic demo projects that don't solve real problems. The advice is to be practical, go deep in one area, deploy real things, and build projects that address genuine use cases.