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AI Engineer - Interview book

AI Engineer - Interview book
Jitin Kayyala

You already know how to build software.

You can design APIs, debug production incidents, reason about latency, operate databases, and ship features that people rely on. But AI engineering interviews ask a different set of questions:

How do you design a RAG system that does not confidently invent answers? When should you use an agent—and when is a simple workflow safer? How do you evaluate an LLM feature when quality can decline without an error message? How do you control token costs, defend against prompt injection, and safely deploy a prompt change?

AI Engineer – Interview Book bridges the gap between being a capable software engineer and being ready for an AI engineering interview.

Learn how to reason about models, retrieval, agents, evaluations, cost, latency, safety, and production failure modes. Practice answers out loud. Learn the trade-offs behind the right answer—not just the vocabulary.

Stop preparing for yesterday’s system-design interview. Start preparing to build AI systems that work in the real world.

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About

About

About the Book

AI Engineer – Interview Book is a practical interview-preparation guide for software engineers moving into AI engineering.

It assumes you already know how to build and operate software: APIs, databases, caching, queues, testing, CI/CD, reliability, and production incident response. Rather than asking you to become an ML researcher, it helps you learn the AI-specific concepts and trade-offs that modern AI engineering interviews actually test.

You’ll build confidence in the areas that repeatedly determine interview outcomes:

  • LLM fundamentals, embeddings, and transformer concepts
  • Prompt and context engineering for production systems
  • Retrieval-Augmented Generation (RAG), hybrid search, chunking, and vector stores
  • Agents, tool use, guardrails, and multi-step workflows
  • AI system design, including latency, cost, reliability, safety, and observability
  • Evaluation systems, golden datasets, LLM-as-a-judge, regression testing, and quality monitoring
  • Coding exercises, portfolio projects, behavioural questions, and interview strategy

This is not a textbook full of abstract theory or a collection of answers to memorise. It is a question-driven guide built around the reasoning interviewers look for. Every chapter contrasts plausible but weak answers with strong, production-minded answers—and prepares you for the follow-up questions where interviews are often decided.

By the end, you will be able to explain, design, test, and defend AI-powered systems with the judgment of an engineer who knows how to ship them.

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Artificial Intelligence

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Author

About the Author

Jitin Kayyala

Jitin Kayyala is a Sun Certified Java Developer with over 15 years of experience designing and developing enterprise-scale web applications. He specializes in cloud-native architecture, microservices, and DevOps practices. His technical leadership spans Java, Spring Boot, AWS, Kubernetes, and distributed systems architecture.

Jitin holds multiple professional certifications including AWS Certified Solutions Architect and Oracle Certified Associate.

Contents

Table of Contents

The AI Engineer Interview Book

  1. Introduction: the mismatch that fails good engineers

Part I — From software engineer to AI engineer

Chapter 1: Why AI engineering is a natural next step

  1. What AI engineers actually do

Chapter 2: The AI engineering interview loop

  1. The rounds

Part II — Core ML and LLM knowledge for engineers

Chapter 3: ML fundamentals, engineer’s version

  1. Why this chapter is short

Chapter 4: Deep learning and transformers without drowning

  1. How much you actually need

Chapter 5: LLMs, embeddings, and RAG

  1. Tokens, context, and cost

Part III — Prompting, RAG, and agentic systems

Chapter 6: Prompting and context engineering for production

  1. Reframe: prompts are interfaces, not incantations

Chapter 7: RAG system design in depth

  1. Chunking, properly

Chapter 8: Agents and multi-step workflows

  1. The three levels

Part IV — Coding and implementation

Chapter 9: Coding rounds for AI engineers

  1. What hasn’t changed

Chapter 10: LLM and RAG coding exercises

  1. Exercise 1: minimal semantic search from scratch

Part V — AI system design

Chapter 11: From general system design to AI system design

  1. What transfers, unchanged

Chapter 12: Designing LLM-powered systems

  1. The framework

Chapter 13: Cost, latency, and reliability trade-offs

  1. Why this chapter decides seniority

Part VI — MLOps and LLMOps

Chapter 14: Deployment and serving

  1. What you already know

Chapter 15: Observability, evals, and governance

  1. The core problem, stated once more

Part VII — Behavioral, portfolio, and strategy

Chapter 16: Behavioral questions

  1. Why this round matters more than you think

Chapter 17: Building an AI portfolio

  1. Why one real project outweighs ten tutorials

Chapter 18: The preparation plan

  1. Choose your timeline
  2. Closing

Appendix A: The rules

  1. Appendix B: Question index
  2. Appendix C: Glossary

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