IgniteTech AI Engineer Interview Questions

Design a feature store that lets research teams define, reuse, and serve consistent ML features across training and inference.

Feature EngineeringFeature StoreModel ServingIgniteTech

Design an enterprise RAG system that balances retrieval quality, grounded answers, and low latency over frequently changing internal data.

latencyRAG pipelinesAccuracyIgniteTech

Explain how to train and evaluate a classifier when the positive class is rare and accuracy is misleading.

ExperimentationFeature EngineeringSupervised LearningIgniteTech

Validate a JSON string and return its parsed structure using strict JSON decoding.

Codingjson parsingStringsIgniteTech

Choose a decision threshold for a classifier using precision, recall, calibration, and confusion matrix tradeoffs.

Confusion MatrixPrecisionThreshold TuningIgniteTech

Explain how supervised and unsupervised learning differ, and ground the distinction in a practical ML example.

Unsupervised LearningFeature EngineeringBias-Variance TradeoffIgniteTech