Axsys IT AI Engineer Interview Questions
The questions to prepare for a Axsys IT AI Engineer interview. Questions from real interview reports rank first. Updated daily.
Tests understanding of loss behavior and how it affects performance and calibration in ML models.
Explain vanishing gradients in deep networks and how residual connections, batch normalization, and activation choice improve training.
Design an on-device ML optimization system that balances model quality, latency, memory, power, and rollout safety on mobile hardware.
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Design a real time monitoring and alerting approach for feature drift, model degradation, and noisy metric movement in production.
Approach for monitoring a model in production and spotting drift, threshold issues, and calibration loss.
Explain how embeddings and vector databases fit into a retrieval pipeline for grounded AI responses.
Discuss how to build ML pipelines that are repeatable, traceable, and observable across training and deployment.
Explain a practical approach to fine-tuning an LLM for a specific task, including data, evaluation, and hallucination risks.