AI Engineer
Difficulty: High
System design, LLM integration, Python coding, and ML fundamentals. Expect a mix of take-home and live technical rounds.
ML Engineer
Difficulty: High
Statistics, model evaluation, feature engineering, and coding. Often includes a take-home ML problem and system design round.
LLM Engineer
Difficulty: High
GenAI fundamentals, RAG pipeline design, evaluation frameworks, and LLM system architecture.
Prompt Engineer
Difficulty: Medium
Live prompting exercises, evaluation methodology, and systematic iteration. Portfolio quality matters significantly.
MLOps Engineer
Difficulty: High
Infrastructure, CI/CD for ML, monitoring, and model serving. Expect DevOps depth combined with ML knowledge.
AI Researcher
Difficulty: Very High
Research presentation, paper critique, maths fundamentals, and coding. The most academically rigorous process.
AI Product Manager
Difficulty: Moderate–High
Product sense, AI literacy, case studies, and stakeholder management. Less coding, more decision-making under ambiguity.
How to use these guides
Read the process overview
Understand what to expect at each stage — from initial screen through to offer. Knowing the format lets you prepare the right material.
Work through the questions
Don't just read the answers. Write your own response first, then compare. The gaps between your answer and the example reveal where to study.
Evaluate the employer
The red flags section gives you questions to ask and signals to watch for. A great interview is a two-way process.