AI Infrastructure Engineer: The Interview Guide: GPU Clusters, Distributed Training, and Inference Systems - 15 Design Case Studies, 20 Debugging Scenarios, 250+ Questions with Model Answers

The interview book for the engineers who keep GPUs running.AI infrastructure interviews test what general system design books skip. Can a 70B model fit on your GPUs? Why does a 1,000-GPU training job run at the speed of its slowest node? What happens to latency when a customer starts sending 30,000-token prompts? How do you find the one GPU in ten thousand that is quietly returning wrong answers?This book prepares you for every round of the AI infrastructure loop: GPU platform, training infrastructure, inference and serving, ML platform, and AI SRE roles.Inside you will find: - 15 design case studies: multi-tenant GPU clusters, checkpointing for 10,000-GPU runs, an LLM inference platform at 100,000 QPS, multi-region failover, cost attribution, elastic training on spot capacity, and more. Each one is worked through the same ten-step template and closes with What the Interviewer Is Listening For.- 20 live-debugging scenarios: hung collectives, silent data corruption, tail-latency spikes, fragmented schedulers, failing racks. Each gives the symptom, the data you're shown, the questions that earn credit, and the root cause.- Back-of-the-envelope arithmetic: the numbers worth memorising, six estimation recipes, and fifteen timed drills.- The CAPS framework (Constrain, Arithmetic, Propose, Stress), with a strong and a weak answer compared line by line.- Foundations to interview depth: GPUs, networking, storage, parallelism, inference, and scheduling.- 12 practical coding problems with complete, tested Python solutions.- Behavioural and career chapters: the incident story, 40 behavioural questions, staff-level expectations, resume rewrites, prep plans, and negotiation.- 250+ questions with model answers, including a 100-question rapid-fire bank.Who it's for: SRE, HPC, backend, DevOps, and platform engineers moving into AI infrastructure; ML engineers who keep failing the infrastructure round; and hiring managers building an interview loop.