Lilly Tech Systems
AI Infrastructure Interview Prep
Prepare for AI and ML infrastructure engineering interviews at top tech companies. From GPU clusters and distributed training to Kubernetes orchestration, cloud AI services, and high-performance storage — real interview questions with detailed answers that reflect what hiring teams actually ask in 2025–2026.
Your Learning Path
Start with the AI infrastructure interview landscape, master GPU compute and distributed systems, then tackle Kubernetes, cloud services, and storage/networking questions.
1. AI Infrastructure Roles
Role types, skills expected, interview formats at top companies, and how to structure your preparation for AI infrastructure engineering positions.
2. GPU & Compute Questions
12 Q&A covering GPU architecture, CUDA basics, GPU memory management, multi-GPU training, GPU vs TPU comparisons, and cost optimization strategies.
3. Distributed Training Questions
10 Q&A on data parallelism, model parallelism, AllReduce, NCCL, DeepSpeed, FSDP, fault tolerance, and scaling training to thousands of GPUs.
4. Kubernetes for ML Questions
10 Q&A covering GPU scheduling, Kubernetes operators, job queuing with Volcano and Kueue, autoscaling, and resource management for ML workloads.
5. Cloud AI Services Questions
10 Q&A on SageMaker, Vertex AI, Azure ML, managed vs self-hosted trade-offs, cost comparison, and production architecture patterns across clouds.
6. Storage & Networking for AI
10 Q&A on distributed file systems, object storage, data loading bottlenecks, network bandwidth for training, RDMA, and InfiniBand.
7. Practice Questions & Tips
Rapid-fire questions, infrastructure design challenges, FAQ accordion, and strategic tips for acing your AI infrastructure interview from preparation to offer.
What You'll Learn
By the end of this course, you will be able to:
Master GPU Infrastructure
Explain GPU architecture, CUDA programming concepts, memory hierarchies, multi-GPU configurations, and cost optimization strategies for large-scale AI training clusters.
Design Distributed Training
Architect distributed training systems using data parallelism, model parallelism, pipeline parallelism, and hybrid approaches with frameworks like DeepSpeed and FSDP.
Orchestrate ML on Kubernetes
Configure GPU scheduling, resource quotas, job queuing, autoscaling, and operators for running large-scale ML training and inference workloads on Kubernetes clusters.
Evaluate Cloud AI Platforms
Compare SageMaker, Vertex AI, and Azure ML. Make informed decisions between managed and self-hosted infrastructure with production-grade architecture patterns.