Becoming an AI engineer isn't a quick 3-month sprint, contrary to popular online claims. The reality involves significant foundational work. Egor, a machine learning engineer and career coach, emphasizes this in a detailed guide. He clarifies the role: AI engineers specialize in integrating existing foundational models like GPT or Claude into applications, not building them from scratch. This requires deep software engineering skills alongside AI knowledge, making the transition challenging without prior experience.

Breaking in demands prior expertise. It's extremely difficult to enter AI engineering directly with zero background. Egor recommends starting as a software engineer or data scientist for at least one year. Greg Brockman (OpenAI CTO) supports this approach. The timeline involves securing a software engineering role (6 months possible with effort) and then dedicating another year to mastering AI fundamentals before pivoting. This ensures a solid grasp of core concepts.

Learning the essentials is crucial. While a PhD isn't needed, understanding maths fundamentals (statistics, linear algebra), supervised/unsupervised learning, and neural networks provides the necessary background. Resources like the Harvard CS50 course and "Hands-On ML" book are recommended. Integrating this learning with full-time work is key.

Deep dive into AI engineering comes next. Focus on AI APIs, prompt engineering, and retrieval-augmented generation (RAG). LangChain is highlighted as the key Python package. Books like "Hands-On Large Language Models" and "AI Engineering" offer deeper insights. The journey requires sustained effort, not shortcuts.