
Targeting AI/ML or GenAI roles? Let me test you on what companies actually ask in these interviews.
What we'll cover:
- ML fundamentals (depending on your role: classical ML, deep learning, or LLM-focused)
- System design for AI features (how would you build a RAG pipeline? an agent system?)
- Practical scenario questions from real GenAI engineering interviews
- Conceptual depth (transformers, embeddings, attention, fine-tuning tradeoffs)
- Detailed feedback on gaps and exactly what to study
I build production LLM systems daily and teach this stack to students. I know what's expected at different levels.
Best for: Students applying for AI/ML engineer, GenAI engineer, or data science intern roles.