Gnani.ai AI Engineer Interview Questions
The questions to prepare for a Gnani.ai AI Engineer interview. Questions from real interview reports rank first. Updated daily.
Design an enterprise RAG pipeline for EXL Service clients that reduces hallucinations through retrieval, grounding, evaluation, and safety controls.
Design and implement a safe, evaluable multi-agent system for complex reasoning under cost, latency, and hallucination constraints.
Explain when to use prompt engineering versus fine-tuning, including quality, cost, latency, data, and maintenance tradeoffs.
Design a production monitoring strategy that detects data, concept, prediction, and performance drift before it harms users.
Design an LLM serving architecture that balances throughput, latency, batching, memory use, and reliability.
Design an LLM serving architecture that manages long generation latency, GPU efficiency, reliability, and model quality.
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Select and justify the evaluation metrics needed to move an LLM from prototype testing to reliable production monitoring.
Explain how embeddings and vector databases fit into a retrieval pipeline for grounded AI responses.