ML / AI Engineer Interview Prep & Rubrics

ML fundamentals and evaluation discipline, MLOps, and the LLM and RAG questions now standard in AI-product loops.

The loop, in order

1. 1Machine Learning24 questions · 8 topicsThe modelling half of an ML loop: supervised and unsupervised methods, features and leakage, metric choice, imbalance, deep learning foundations, forecasting and ranking.
2. 2MLOps & ML Platform24 questions · 8 topicsEverything between a model that works in a notebook and one a business depends on: reproducibility, feature and training infrastructure, registries, serving, drift, cost and governance.
3. 3Generative AI & Large Language Models26 questions · 7 topicsPrompting and structured output, context and tokens, adaptation, evaluation, inference economics, injection defence, and running LLM features in production.
4. 4RAG, Retrieval & Agents24 questions · 7 topicsRetrieval-augmented generation end to end: ingestion, embeddings and vector indexes, hybrid search and reranking, retrieval evaluation, permissions, and agentic tool use.
5. 5Data & AI Engineering40 questions · 8 topicsData engineering, ML engineering and LLM/RAG interview questions covering pipelines, model evaluation, overfitting, retrieval, embeddings, vector search and production AI systems.
6. 6Python22 questions · 7 topicsPython for backend, automation, and data roles, including the runtime behaviour that surprises candidates who only know the syntax.
7. 7System Design70 questions · 12 topicsOpen-ended design rounds broken into the building blocks they draw on, plus full case studies worked end to end with explicit trade-off reasoning.
8. 8Behavioural & Culture Fit25 questions · 8 topicsThe round candidates most often under-prepare, treated as a skill with structure rather than small talk, including the rubric interviewers score against.
9. 9HR, Screening & Offer19 questions · 6 topicsThe recruiter and HR rounds that gate the technical loop and decide your compensation, where most candidates improvise and lose money.