Perigon Search AI Engineer Interview Questions
The questions to prepare for a Perigon Search AI Engineer interview. Questions from real interview reports rank first. Updated daily.
Design a low-latency, memory-efficient cache for storing and retrieving high-dimensional vector embeddings at scale.
Architect a reliable multi-agent system in which specialized agents coordinate to complete one complex task.
Design an LLM serving platform that balances inference cost, latency, quality, reliability, and user experience.
Design a low-latency, high-concurrency LLM serving layer with effective prompt, response, and KV-cache strategies.
Tests conflict resolution and prioritization when balancing model quality, delivery speed, and stakeholder needs.
Select and justify the evaluation metrics needed to move an LLM from prototype testing to reliable production monitoring.
Explain how to manage memory, summarization, retrieval, and safety in a long-running LLM agent when context exceeds the model window.
Design a high-throughput RAG pipeline that grounds answers, detects unsupported claims, and balances quality, latency, and cost.
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