What this interview will probe
Build the LLM serving infrastructure that processes trillions of tokens per week across partner models (OpenAI, Anthropic, Gemini) and self-hosted open models, improving reliability, latency, and efficiency of distributed AI workloads. The role spans scalable APIs, GPU orchestration, and real-time serving systems built with tools like vLLM, Ray, and MLflow. An interview would explore designing high-throughput low-latency serving systems, GPU resource scheduling and batching strategies, and service-oriented backend architecture for inference at massive scale.
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