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The Complete AI Engineer Interview Playbook
Paperback
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ISBN13: 9798258476258
Publisher: Independently Published
Published: Apr 22 2026
Pages: 398
Weight: 1.17
Height: 0.82 Width: 6.00 Depth: 9.00
Language: English
Publisher: Independently Published
Published: Apr 22 2026
Pages: 398
Weight: 1.17
Height: 0.82 Width: 6.00 Depth: 9.00
Language: English
The interview prep guide built for developers who already know how to build software - and now need to prove they can build AI. You've spent years shipping production systems. Now every job posting says AI Engineer. The problem? Most interview prep is either too academic (research papers you'll never use) or too shallow (chatbot tutorials that won't survive a senior-level interview). This book is different. It's built specifically for working developers transitioning into AI engineering roles. 188 interview questions across 11 chapters, each answered at three levels:
- Junior - Clear, confident fundamentals
- Mid-Level - Practical tradeoffs and tooling knowledge
- Senior - Production architecture, cost thinking, and failure modes Every question also includes follow-up probes interviewers actually ask, red-flag answers to avoid, and a senior insight that separates good candidates from great ones. What's inside:
- LLM Fundamentals - Transformers, tokenization, embeddings, hallucination, decoding strategies
- RAG & Vector Databases - Chunking, hybrid search, reranking, RAGAS evaluation, pgvector vs Pinecone
- Agentic AI & Multi-Agent Systems - ReAct, Plan-and-Execute, tool use, guardrails, PydanticAI, human-in-the-loop
- Model Context Protocol (MCP) - Servers, clients, transports, FastMCP, A2A, ACP, enterprise deployment
- LLM Fine-Tuning - LoRA, QLoRA, DPO, synthetic data generation, evaluation pipelines, production serving
- MLOps / LLMOps - Prompt versioning, cost optimization, drift detection, CI/CD for LLMs, A/B testing
- Context Engineering - Token budgets, dynamic injection, compression, chain-of-thought, context poisoning
- AI Platforms & Tools - Claude vs GPT-4o vs Gemini, Claude Code, Cursor, Anthropic API, AWS Bedrock, Azure OpenAI
- System Design for AI - RAG at scale, real-time assistants, regulated industry AI, multi-model pipelines, capacity planning
- AI Engineering Toolchain - Hugging Face, LlamaIndex, PyTorch, FastAPI, MLflow, W&B, FAISS, Semantic Kernel, n8n
- Behavioural & Situational - Stakeholder management, production failures, technical disagreements, mentoring, career strategy Mapped to real job board requirements. Every chapter was built by analyzing what companies are actually hiring for - not what textbooks think matters. Who this book is for:
- Full-stack developers pivoting to AI engineering
- C#/.NET, Python, or JavaScript developers adding AI to their skill set
- Senior engineers preparing for AI-focused interviews at any level
- Working professionals who need to get interview-ready without reading five 500-page textbooks Not a tutorial. Not a textbook. A playbook. Written by Lakhpreet Singh, a software engineer with 20+ years of experience across the US and Canada who made the transition himself.
- Mid-Level - Practical tradeoffs and tooling knowledge
- Senior - Production architecture, cost thinking, and failure modes Every question also includes follow-up probes interviewers actually ask, red-flag answers to avoid, and a senior insight that separates good candidates from great ones. What's inside:
- LLM Fundamentals - Transformers, tokenization, embeddings, hallucination, decoding strategies
- RAG & Vector Databases - Chunking, hybrid search, reranking, RAGAS evaluation, pgvector vs Pinecone
- Agentic AI & Multi-Agent Systems - ReAct, Plan-and-Execute, tool use, guardrails, PydanticAI, human-in-the-loop
- Model Context Protocol (MCP) - Servers, clients, transports, FastMCP, A2A, ACP, enterprise deployment
- LLM Fine-Tuning - LoRA, QLoRA, DPO, synthetic data generation, evaluation pipelines, production serving
- MLOps / LLMOps - Prompt versioning, cost optimization, drift detection, CI/CD for LLMs, A/B testing
- Context Engineering - Token budgets, dynamic injection, compression, chain-of-thought, context poisoning
- AI Platforms & Tools - Claude vs GPT-4o vs Gemini, Claude Code, Cursor, Anthropic API, AWS Bedrock, Azure OpenAI
- System Design for AI - RAG at scale, real-time assistants, regulated industry AI, multi-model pipelines, capacity planning
- AI Engineering Toolchain - Hugging Face, LlamaIndex, PyTorch, FastAPI, MLflow, W&B, FAISS, Semantic Kernel, n8n
- Behavioural & Situational - Stakeholder management, production failures, technical disagreements, mentoring, career strategy Mapped to real job board requirements. Every chapter was built by analyzing what companies are actually hiring for - not what textbooks think matters. Who this book is for:
- Full-stack developers pivoting to AI engineering
- C#/.NET, Python, or JavaScript developers adding AI to their skill set
- Senior engineers preparing for AI-focused interviews at any level
- Working professionals who need to get interview-ready without reading five 500-page textbooks Not a tutorial. Not a textbook. A playbook. Written by Lakhpreet Singh, a software engineer with 20+ years of experience across the US and Canada who made the transition himself.
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