Ace Your AI/ML & GenAI Interview

Technical Fundamentals

ML theory, not generic programming: bias-variance trade-off, overfitting/regularization, evaluation metrics (precision/recall/AUC and when each matters), feature engineering, train/test discipline and leakage, classical models vs deep learning trade-offs, embeddings and LLM-era basics (fine-tuning vs RAG).

Problem Solving

Applied-ML coding, NOT generic array/string DSA: pandas/numpy data manipulation, implementing a small ML routine from scratch (k-means step, gradient-descent update, train/test split), SQL aggregation for a modeling need, cleaning a messy dataset with edge cases.

Projects & Behavioral

AI/ML product deep-dive: how they integrated a model (classical or LLM-based) into a real product feature, build-vs-buy/vendor-model reasoning, production monitoring and drift response, a time the model's real-world behavior diverged from offline metrics, communicating model limitations to non-technical stakeholders.

HR Round

Motivation for ML work amid hype, research-vs-engineering positioning, communicating uncertainty to stakeholders.

System Design

ML system design, not generic backend: recommendation-system architecture, feature-store design, training-vs-serving skew, model-serving infrastructure (batch vs real-time), A/B testing and rollback of models, monitoring for drift.