How to Crack the AI Engineer Interview

A map of what AI engineer and GenAI interviews actually test, and the study path through it: machine learning foundations, transformer internals, efficiency, alignment and fine-tuning, prompting and retrieval, evaluation, and the failure modes every interviewer asks about.

Undergrad to Professional · English · 17 chapters

• Course Overview — What AI engineer and GenAI interviews test, how the topics fit together, and a reading path through the rest of Sahi Padhai that covers each one properly.
• What Gets Tested — The rounds you are likely to face, what each one is really assessing, and why the same topic is asked very differently depending on who is asking.
• A Study Plan — A four-week route through the material, what to do if you have less time, and how to study so that the knowledge survives a follow-up question.
• Machine Learning Foundations — The classical material still gets asked, and usually as a diagnostic: bias and variance, regularisation, generative versus discriminative, and knowing which algorithm suits which problem.
• Training Neural Networks — Backpropagation, gradient descent and the optimizers built on it, plus the practical machinery — initialisation, activations, normalisation — that makes a deep stack trainable at all.
• Tokens, Embeddings and the Block — What a language model is, how text becomes numbers, and the structure of the block that everything else repeats — the groundwork the attention questions assume.
• Attention — The most-asked topic in a GenAI interview, and the one that cannot be bluffed: queries, keys and values, why scores are scaled, what masking does, how heads divide the work, and how position gets back in.
• Generation and Decoding — How the model's final vector becomes an actual word, what temperature and top-p really control, and why the same model can be deterministic or creative depending on three numbers.
• Making Inference Affordable — Why serving a model costs what it does, and the techniques that bring it down: the KV cache and its shrinking variants, sparse experts, low precision, pruning and distillation.
• Alignment — Why a pretrained model is not yet an assistant, what supervised fine-tuning can and cannot do, and how RLHF and DPO use human preferences to close the gap.
• Fine-Tuning, LoRA and Transfer — What full fine-tuning costs, why a low-rank update is usually enough, how QLoRA fits a large model onto one accelerator, and when transfer learning is the wrong instinct.
• Training Data and Synthetic Data — Every method in the previous two chapters needs data that usually does not exist. Where teams get it, how models generate it for each other, and the failure modes that make it dangerous.
• Prompting and In-Context Learning — Few-shot examples, chain-of-thought and self-consistency — what each actually does to the model, and why 'in-context learning' involves no learning at all.
• Retrieval-Augmented Generation — The architecture most AI engineering roles are actually built around: what RAG solves, every stage of the pipeline, and the design questions interviewers use to find out whether you have shipped one.
• Agents and Tool Use — Giving a model tools and a loop, what that buys, and the four ways it goes wrong — the area where interviewers most often ask you to debug rather than describe.
• Evaluation — Perplexity, BLEU and ROUGE, preference ranking, and RAG-specific measurement — what each metric sees, what it is blind to, and why 'how would you know it works?' is the question that separates candidates.
• Failure Modes and Safety — Why models hallucinate, why safety training can be talked past, and what looking inside a model has revealed — including that its stated reasoning need not match what it actually did.