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AI Engineer Interview: A Comprehensive Guide to Generative AI, LLMs, RAG, AI Agents, Machine Learning, MLOps, and AI System Design
Paperback
Publisher: Independently Published
Published: Sep 19 2026
Pages: 542
Weight: 2.73
Height: 1.09 Width: 8.50 Depth: 11.00
Language: English
Prepare for the modern AI engineering interview-from machine learning fundamentals to production-scale generative AI systems.
AI Engineer Interview is a comprehensive preparation guide for candidates pursuing AI Engineer, Generative AI Engineer, Machine Learning Engineer, LLM Engineer, RAG Engineer, MLOps Engineer, and senior AI architecture roles. It combines essential theory, practical implementation, system design, and interview-focused questions in one structured resource.
Beginning with machine learning, deep learning, and transformer fundamentals, the book progresses into the technologies shaping today's AI applications: large language models, prompt and context engineering, fine-tuning, embeddings, vector databases, retrieval-augmented generation, AI agents, function calling, and the Model Context Protocol (MCP).
You will learn how to:
- Explain machine learning, neural networks, transformers, and LLMs clearly
- Choose between prompting, RAG, fine-tuning, LoRA, and QLoRA
- Build and improve production RAG pipelines
- Design tool-using agents and multi-agent systems
- Implement memory, planning, reflection, and human approval workflows
- Deploy and optimize models using containers, Kubernetes, GPUs, caching, batching, quantization, and autoscaling
- Apply MLOps, LLMOps, observability, evaluation, and feedback loops
- Address prompt injection, data leakage, guardrails, authorization, bias, and AI governance
- Design scalable RAG systems, enterprise AI assistants, agent platforms, and LLM-serving architectures
- Solve practical Python, SQL, embedding-search, reranking, semantic-caching, tool-calling, and evaluation problems
Whether you are entering AI engineering, transitioning from software or data engineering, or preparing for a senior-level position, this guide will help you connect foundational concepts with the practical decisions expected in real interviews and production AI systems.
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