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Cracking the GenAI / LLM Engineer Interview: Concepts, System Design & 150+ Questions with Model Answers
Paperback
Series: Placement Preparation for Computer Science Engineers
CareersGeneral ComputersProgramming
Publisher: Independently Published
Published: Sep 6 2026
Pages: 198
Weight: 0.60
Height: 0.42 Width: 6.00 Depth: 9.00
Language: English
Companies are hiring AI engineers faster than ever, and the interview loop looks nothing like a standard backend loop. You will be asked why your RAG pipeline returns irrelevant chunks, how you would cut inference cost by half, how you would prove a prompt change actually improved quality, and how you would design a document question-answering system for a million users. Most strong engineers fail these interviews not because they lack ability, but because they prepared for the wrong questions.
This book fixes that mismatch in three to four weeks of focused study. It is written for software engineers with one to eight years of experience who understand APIs, databases and distributed systems, but have not yet worked deeply with large language models. No PhD, no maths-heavy theory, no model training required.
What is inside- 15 chapters mapped to the real interview loop - recruiter screen, technical screen, conceptual round, AI system design, coding round, project deep-dive and bar raiser
- 150+ interview questions with model answers, written the way a strong candidate would actually say them out loud, each with a note on what the interviewer is really checking
- 5 fully worked AI system designs, end to end, with the trade-offs explained
- Python coding problems common in GenAI interviews - chunking, parsing, retries, rate limiting, scoring
- Clear diagrams you can redraw on a whiteboard from memory
- Common misconceptions and trap questions - the exact places candidates lose points
- Cheat sheets, a full mock interview and a final-week revision plan
Transformers and attention at interview level; tokenization, embeddings and vector similarity; prompt engineering as an engineering discipline; retrieval-augmented generation (RAG) end to end; vector databases and retrieval quality; fine-tuning vs RAG vs prompting; evaluating LLM systems; agents, tool use and function calling; hallucination, safety, guardrails and responsible AI; LLMOps - serving, latency, caching and cost optimization; behavioral and project deep-dive questions for AI roles.
How to use itFour weeks: one chapter a day, then system design and coding, then mock interviews. One week: the five chapters that appear in almost every loop. One evening: the final chapter and the key takeaways. Every chapter follows the same structure, so you always know where you are.
If you can explain a load balancer, a cache and a message queue, you already have the foundation this book builds on. Start today and walk into your loop prepared.
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