Best books for AI Engineers

Hands-On Large Language Models

by Jay Alammar, Maarten Grootendorst · 2024

~400 pages~10 hrs to read

A visual, code-first tour of tokens, embeddings, text generation, semantic search, RAG and fine-tuning. Read it first if you can program but have not yet built on language models.

Prompt Engineering for LLMs

by John Berryman, Albert Ziegler · 2024

~250 pages~6 hrs to read

Covers how models read a prompt and how to assemble context, structure instructions and build prompts into applications. Good early on, when most of your output is prompts and the glue around them.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

by Aurélien Géron · 2017

~860 pages~22 hrs to read

A broad, code-heavy grounding in classical machine learning and neural networks. Work through it to understand what sits underneath the model APIs you will be calling.

AI Engineering

by Chip Huyen · 2025

~350 pages~9 hrs to read

Covers building applications on foundation models: evaluation, prompting, RAG, finetuning, inference optimisation and the architecture around them. The closest thing to a textbook for the role; read it once you have shipped a first feature.

LLM Engineer's Handbook

by Paul Iusztin, Maxime Labonne · 2024

Walks an LLM application through data collection, fine-tuning, deployment, RAG and monitoring as one end-to-end system. Useful when you are moving from a demo to something that has to run and be maintained.

Build a Large Language Model (From Scratch)

by Sebastian Raschka · 2024

Builds a GPT-style model step by step in PyTorch, from tokenisation and attention to pretraining and finetuning. Read it when you want to know why the models behave as they do, not just how to call them.

AI-Powered Search

by Trey Grainger, Doug Turnbull, Max Irwin · 2024

~520 pages~13 hrs to read

Explains how to combine search relevance, embeddings, learning to rank and LLMs into retrieval systems. The best fit if your work leans on RAG and you want retrieval that holds up, not just a vector lookup.

Natural Language Processing with Transformers

by Lewis Tunstall, Leandro von Werra, Thomas Wolf · 2022

A practical guide to the Hugging Face ecosystem: classification, named entity recognition, summarisation, question answering and training your own transformer. Read it when you need to go beyond hosted APIs and work with open models.

Designing Machine Learning Systems

by Chip Huyen · 2022

~390 pages~10 hrs to read

Covers data pipelines, feature engineering, deployment, monitoring and iteration for production ML. Read it when you own a system, not just a model, and need to reason about what breaks over time.

Designing Data-Intensive Applications

by Martin Kleppmann · 2017

~620 pages~16 hrs to read

The standard reference on storage, replication, partitioning, transactions and stream processing. Read it once your AI features depend on databases and queues you have to reason about.

Machine Learning Design Patterns

by Valliappa Lakshmanan, Sara Robinson, Michael Munn · 2020

~400 pages~10 hrs to read

Catalogues recurring solutions for data representation, training, serving, reproducibility and responsible AI. Handy when you design a pipeline and want proven patterns to compare against.

The Staff Engineer's Path

by Tanya Reilly · 2022

~520 pages~13 hrs to read

Covers technical leadership without a management title: setting direction, influencing across teams and carrying big projects. Read it when you are moving toward senior or architect-level work.