The AI Engineer Interview Crash Course

Build the technical depth and reasoning fluency that AI engineering interviews actually demand: from transformer internals and alignment techniques to production-grade RAG, agents, and safety systems.

4.7

15 Lessons

Updated 1 month ago

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LEARNING OBJECTIVES

A deep understanding of transformer internals: attention mechanisms, positional encodings, architectural variants like GQA and Flash Attention, and how design choices affect inference cost
Fluency in the full training and alignment pipeline: backpropagation, fine-tuning strategies, RLHF, DPO, and modern techniques like GRPO and Constitutional AI
Practical knowledge of model compression and efficiency: LoRA, QLoRA, quantization, distillation, and understanding when to use each approach
The ability to design and evaluate RAG systems, including retrieval strategies, chunking trade-offs, re-ranking, and common production failure modes
An understanding of agentic architectures: ReAct, tool use, MCP, A2A, and the failure modes that matter when agents operate autonomously
Familiarity with evaluation frameworks, safety risks, and production engineering practices that keep LLM systems reliable at scale

Learning Roadmap

15 Lessons

How AI Models Work

Master AI and LLM fundamentals for AI engineer interviews, covering ML foundations, tokenization, embeddings, attention, and transformer architectures.

Introduction to Crash Course

Machine Learning Foundations

Tokenization and Embeddings

Attention Mechanisms

Transformer Architecture

LLM Training, Fine-Tuning, and Optimization

Learn AI model training, fine-tuning, inference, compression, scaling, and evaluation techniques crucial for AI engineer interviews.

Decoding Strategies and Inference Optimization for LLMs

Fine-tuning and Alignment

Model Compression Techniques for AI Systems

Model Architectures and Scaling

Evaluating AI and LLM Models

AI System Design

5 Lessons

Explore applied AI system design for interviews, including prompt engineering, RAG systems, agentic AI, and model interpretability

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Developed by MAANG Engineers

Every Educative lesson is designed by a team of ex-MAANG software engineers and PhD computer science educators, and developed in consultation with developers and data scientists working at Meta, Google, and more. Our mission is to get you hands-on with the necessary skills to stay ahead in a constantly changing industry. No video, no fluff. Just interactive, project-based learning with personalized feedback that adapts to your goals and experience.

ABOUT THIS COURSE

If you are short on time and need to prepare efficiently, this crash course is a condensed version of the comprehensive Ace The AI Engineer Interviews course, covering the most critical topics in approximately 15 to 20 hours of focused study. AI engineering interviews test whether you can reason from first principles, connect concepts across the stack, and explain trade-offs under pressure. This course is built around that reality. It takes you from transformer internals and the alignment pipeline through to production-grade RAG, agentic systems, and safety engineering, covering everything from how attention works to how a deployed LLM system stays reliable at scale. Every topic was selected using a single filter: Does it appear in real interviews at companies working with large language models? If not, it was excluded.

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