The AI Infrastructure Engineer track covers GPU and hardware architecture, LLM inference optimization (vLLM, TRT-LLM, continuous batching), distributed training (tensor, pipeline, and data parallelism), model serving at scale, and AI platform design. Sessions calibrate by level and probe the depth of your infrastructure trade-offs.
The AI Infra Engineer track covers GPU memory hierarchy and hardware selection, LLM inference optimization with vLLM and TRT-LLM, distributed training strategies (tensor parallelism, pipeline parallelism, FSDP), KV cache management, model serving SLAs, and behavioral questions on building AI infrastructure platforms at scale.
If you describe an inference serving setup, Alex follows up on your batching strategy, KV cache sizing, or how you handle latency SLAs at high throughput. If you describe a distributed training configuration, Alex asks about your communication overlap strategy and how you handle stragglers.
The AI Infrastructure Engineer track focuses on GPU hardware, LLM inference optimization, and distributed training at the systems level. The MLOps Engineer track focuses on ML pipelines, feature stores, and model lifecycle management. Both are separate tracks with distinct question banks.
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