AI Engineer Interview Questions & Answers (2026)

AI is the broad goal of machines performing tasks that need intelligence. ML is a subset where systems learn patterns from data instead of being explicitly programmed. Deep learning is a subset of ML using multi-layer neural networks, which excels at images, text and audio.

Supervised learning trains on labelled data (classification, regression). Unsupervised learning finds structure in unlabelled data (clustering, dimensionality reduction). Reinforcement learning trains an agent via rewards from interacting with an environment.

Overfitting is when a model memorises training data and fails to generalise. Prevent it with more/cleaner data, simpler models, regularisation (L1/L2, dropout), cross-validation, and early stopping. The goal is low gap between train and validation performance.

Bias is error from overly simple assumptions (underfitting); variance is error from sensitivity to training data (overfitting). Increasing model complexity lowers bias but raises variance. You tune capacity and regularisation to minimise total error.

Beyond accuracy (misleading on imbalanced data), use precision, recall, F1, ROC-AUC and the confusion matrix. Choose the metric by the cost of errors — e.g. recall matters for fraud/medical, precision for spam.

RAG augments an LLM with external knowledge: you embed documents into a vector store, retrieve the most relevant chunks for a query, and pass them as context to the model. It reduces hallucination and lets the model use private/up-to-date data without retraining.

Ground answers with RAG, instruct the model to say "I don’t know", lower temperature, add citations, validate outputs with rules or a second model, constrain with structured output/schemas, and evaluate with a test set of known answers.

Package the model and dependencies (e.g. a container), expose it behind an API (FastAPI/serverless), add input validation, batching and caching, monitor latency and accuracy/drift, and version models so you can roll back. Consider GPU vs CPU and cost.

Drift is when production data diverges from training data, degrading performance. Detect it by monitoring input feature distributions and prediction distributions over time (e.g. PSI, KL divergence) and tracking live metrics; retrain when drift crosses a threshold.

Prefer prompting/RAG when you need fresh or private knowledge, fast iteration and low cost. Fine-tune when you need a specific style/format, lower latency/cost at scale, or behaviour that prompting can’t reliably achieve. Often you combine RAG with light fine-tuning.

Ingest and chunk documents, create embeddings, store in a vector DB; on a query, embed it, retrieve top-k chunks (with filters/metadata), build a prompt with context, call the LLM, return the answer with citations. Add caching, evaluation, access control and monitoring.