AI Engineering Fundamentals: Build a Career in Modern AI

AI is transforming software development, but building with artificial intelligence requires a completely different mindset than traditional, deterministic programming. If you want to transition into this rapidly growing field, you need to understand how AI engineers design, test, and deploy systems that learn and adapt. This text-only course guides you from foundational concepts to modern AI workflows, helping you think like an AI engineer. You will learn how to work with non-deterministic outputs, connect large language models to external data, and build reliable applications. What you'll learn: 1. Understand the core differences between traditional software engineering and AI engineering. 2. Learn the fundamentals of large language models (LLMs) and how to craft effective prompts. 3. Explore retrieval-augmented generation (RAG) patterns to ground AI responses in custom data. 4. Configure vector databases to store and query high-dimensional embeddings efficiently. 5. Master basic MLOps concepts to monitor, evaluate, and maintain AI systems in production. 6. Practice designing exploratory workflows to handle non-deterministic system behaviors. The course begins with essential terminology and the foundational shift from deterministic logic to probabilistic AI systems. From there, you will read through step-by-step conceptual breakdowns and clear code snippets that illustrate how to orchestrate modern AI APIs, manage context windows, and evaluate model performance. This course is designed for software developers, tech enthusiasts, and beginners looking to enter the AI space. No prior experience with machine learning or data science is required. Start reading today and build a solid foundation in the principles of modern AI engineering.