Once you're running AI in production, the hard problems are systems problems.
GPUs idling at 5% utilization, and pinning down whether it's data loading, comms overhead, or the scheduler is its own week of work.
Distributed training runs that crash mid-job and leave you debugging whether it was the network, a flaky node, or NCCL acting up. Inference that benchmarks beautifully on a single request and collapses under concurrent load — KV cache pressure, batch dynamics, tail latency you can't reproduce in staging. Stacks stitched together from vLLM, Ray, Triton, and Kubernetes that take more…

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