Machine Learning Interview Guide: Classical Models, Ensembles, and ML in Production

Machine learning interviews in AI engineering roles often focus on hybrid systems. Interviewers ask when XGBoost or logistic regression outperforms LLMs for tabular prediction, ranking, and routing—and how you integrate classical models with embedding-based components.

Ensemble methods and boosting remain workhorses for structured data. Candidates explain hyperparameter sensitivity, handling missing values, monotonic constraints for regulated scoring, and SHAP-based explanations for stakeholder trust.

Calibration matters when model scores drive thresholds—fraud risk, escalation priority, or human review queues. Discuss Platt scaling, isotonic regression, and monitoring calibration drift over time.

Feature engineering and leakage detection still separate strong practitioners. Interviewers present messy enterprise tables with derived features, temporal aggregates, and ask how you validate that features are available at prediction time in production.

ML production concerns include batch versus online inference, model registry practices, and shadow deployments for classical models that silently gate LLM spend. Articulate end-to-end ownership from training notebooks to monitoring dashboards.