Interviews & Careers
Crack the AI Engineer Interview
shashank
For engineers interviewing for AI, ML and GenAI roles. You practice explaining tokens, context windows, temperature and fine-tuning versus RAG, designing a RAG pipeline with chunking, embeddings, a vector database and evaluation, and describing how agents use tool calling and guardrails. You also tell your AI project story and think out loud on coding and system design questions. Tim plays a senior AI engineer who asks follow-ups until your answer is solid.
5 sessions · 3h 50m
Free
This Goal only
Recommended
$20/mo
Subscribe, 10 hours a month
Your learning path
Skills you will learn
Explaining LLM basics clearly
You will meet with a coach to practice translating complex technical terms into simple analogies. You will practice explaining how smart text systems process words, recall information, and control their creativity.
RAG system design
You will meet with a coach to break down how to explain search-based AI systems in an interview. You will practice structuring your thoughts around data preparation, search logic, and quality checks.
Agentic AI and tool use
You will work with a coach to structure your explanations of how independent AI systems make decisions and use tools. You will outline real-world examples of system failures and safety controls for a project you choose.
Your AI project story
You will work with a guide to outline a past project you built. You will learn to highlight your technical decisions and outline what you would improve in under two minutes.
Live coding and system questions
You will meet with your guide to explore strategies for talking through your programming choices. You will practice asking clarifying questions and learning how to handle moments when you do not know the answer.
How goals work
Four steps, one real shift.
Interviews & Careers
Crack the AI Engineer Interview
shashank
For engineers interviewing for AI, ML and GenAI roles. You practice explaining tokens, context windows, temperature and fine-tuning versus RAG, designing a RAG pipeline with chunking, embeddings, a vector database and evaluation, and describing how agents use tool calling and guardrails. You also tell your AI project story and think out loud on coding and system design questions. Tim plays a senior AI engineer who asks follow-ups until your answer is solid.
5 sessions · 3h 50m
Free
This Goal only
Recommended
$20/mo
Subscribe, 10 hours a month