Infobip AI Engineer Interview Questions
The questions to prepare for a Infobip AI Engineer interview. Questions from real interview reports rank first. Updated daily.
Design production monitoring for LLM drift, quality regressions, hallucinations, and prompt injection attempts.
Design a safe, observable multi-agent architecture for complex workflows while controlling latency, cost, hallucination, and prompt injection.
Compare embedding models for vector search across quality, cost, latency, dimensions, domain fit, and operational complexity.
Explain practical techniques for interpreting machine learning predictions, including surrogate models, feature attribution, and model-specific methods.
Design a feature store that keeps batch training and real-time inference consistent, fresh, reliable, and cost-effective.
Explain how to select metrics, validate predictions, and analyze errors when evaluating a machine learning model.
Compare common sorting algorithms by best, average, and worst-case time complexity and explain when each is appropriate.
Design a low-latency, high-concurrency LLM serving layer with effective prompt, response, and KV-cache strategies.
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