AI / Machine Learning Engineer interview practice

Model evaluation trade-offs, data pipelines, feature engineering, and MLOps deployment — the full ML interview loop with follow-up drilling.

Topics this interview probes

Model evaluation Data pipelines Feature engineering Training/serving skew LLM applications MLOps & monitoring

Your offline metrics look great but the online model underperforms. How do you debug training/serving skew?
Explain how you would evaluate an LLM-based feature before shipping it to production.
Design a feature pipeline that stays fresh when the underlying data drifts weekly.

The live interviewer generates fresh, role-tailored questions — and drills down when your answers are vague.