314,552 interview questions from 6,000+ companies.
Design an LLM serving system that balances latency, cost, scalability, and safety for production traffic.
Explain how embeddings and vector databases fit into a retrieval pipeline for grounded AI responses.
Design the infrastructure for a multi-agent system where agents communicate, coordinate work, and recover from non-deterministic failures.
Tests data modeling and database selection skills for AI applications at scale.
Tests system-level performance optimization and data access strategies for AI inference.
Tests practical performance engineering for memory usage in AI pipelines.
Tests software design fundamentals used to build maintainable AI systems.
Tests understanding of OS fundamentals relevant to building reliable AI systems.
Tests Python reasoning and debugging ability for AI engineering tasks.
Tests end-to-end RAG architecture design for production AI solutions.
Tests prioritization and decision-making under time pressure.
Tests concurrency control and correctness under parallel execution in AI services.
Tests ability to reason from data trends and variables to make predictions.
Tests approach to LLM evaluation including offline metrics, testing, and iteration.
