AI Engineering Interviews

Chapter 1. Hello AI Engineering!

A few years ago, adding real AI to a product meant months of work: gather data, label it, train a model, tune it, and pray it survived contact with the real world. Today, that model often already exists. One API call reaches a system that writes code, answers questions, summarizes documents, and reasons through problems that used to demand a dedicated research team.

That did not just make the work easier. It moved the hard part. When a powerful model is one call away, the challenge is no longer training it. It is building a reliable system around it: how you prompt it, how it retrieves accurate information, how it uses tools, how you evaluate it, how you keep it safe, and how you keep it fast and affordable under real traffic. That is the discipline of AI engineering, and it is what this book is about.

This shift did not come from nowhere. AI engineering grew out of machine learning engineering: the practice of taking models from experiments and notebooks into production by building the surrounding systems for data collection, labeling, feature engineering, training, validation, deployment, monitoring, and retraining. Figure 1-1 shows that lifecycle.

That model-centric workflow still matters. But with the rise of foundation models, especially large language models, many teams are no longer training sophisticated models from scratch for every new use case. Instead, they integrate powerful pre-trained models and build systems around them. This shifts ...