Kody Technolab AI Engineer Interview Questions
The questions to prepare for a Kody Technolab AI Engineer interview. Questions from real interview reports rank first. Updated daily.
Explain a production RAG pipeline and techniques for reducing unsupported or fabricated answers.
Design a production LLM serving system that balances latency, throughput, cost, reliability, and safe degradation.
Compare embedding models for vector search across quality, cost, latency, dimensions, domain fit, and operational complexity.
Outline the main components of an LLM training data pipeline, from ingestion and cleaning to tokenization, quality checks, and orchestration.
Design an LLM serving architecture that balances throughput, latency, batching, memory use, and reliability.
Design a practical framework for comparing LLM quality, reliability, latency, cost, and safety in production.
Explain practical ways to train and evaluate a classifier when the target classes are highly imbalanced.
Explain which classification metrics to use and how metric choice depends on the business objective and error tradeoffs.
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