Technical practice
Designing Large-Scale GPU Clusters
What to coverDiscuss your experience managing clusters with 1,000+ nodes. Focus on architectural considerations for high-performance fabrics like InfiniBand NDR 400Gb/s, multi-rail design strategies, and ensuring low-latency communication for distributed AI training.
HPC Scheduler Optimization
What to coverCompare the use of SLURM versus PBS Pro in a multi-tenant AIaaS environment. Explain how you would configure these schedulers to handle heterogeneous XPU workloads while maintaining high utilization and fair-share policies.
Containerization in HPC
What to coverExplain the trade-offs between using Singularity and Kubernetes for HPC and AI workloads. Suggestion: Focus on how Singularity addresses security and performance requirements in shared HPC environments compared to standard Docker/Kubernetes setups.
Infrastructure-as-Code for Hybrid Cloud
What to coverDescribe your approach to using Terraform and Ansible to manage hybrid environments spanning on-premises supercomputing and public cloud (Azure/AWS). Address how you maintain configuration baselines and handle state management across these disparate infrastructures.
GPU Performance Tuning
What to coverDetail your process for identifying and resolving bottlenecks in H100/A100 GPU clusters. Mention specific tools or metrics you monitor, such as memory bandwidth, interconnect saturation, or kernel execution efficiency using CUDA profiling tools.
Storage Architectures for AI/HPC
What to coverEvaluate the suitability of different parallel file systems like Lustre, GPFS, or Weka for high-throughput AI training. Discuss how you integrate these with NVMe-oF to support the I/O demands of large-scale model training.
MLOps and AI Framework Integration
What to coverExplain how you integrate Kubeflow or Azure ML with existing HPC infrastructure. Focus on the challenges of enabling distributed training for frameworks like PyTorch or JAX while ensuring seamless data pipeline integration.
Network Fabric Design
What to coverContrast the implementation of InfiniBand versus RoCEv2 in a high-performance cluster. Suggestion: Discuss the design requirements for congestion control and packet loss prevention in multi-planar network architectures.
Behavioural practice
Managing Complex Stakeholder Requirements
What to coverDescribe a situation where you had to align HPC infrastructure design with conflicting performance and cost requirements from different business units. Explain how you balanced these needs to deliver a scalable solution.
Troubleshooting Critical Infrastructure Failures
What to coverProvide an example of a high-severity incident in a production cluster environment. Detail your systematic approach to root cause analysis, the steps taken to restore service, and how you prevented recurrence.
Driving Technical Strategy
What to coverShare an experience where you defined an enterprise-wide HPC or AI infrastructure strategy. Explain how you communicated the technical roadmap to non-technical stakeholders and secured buy-in for the proposed architecture.
Adapting to Rapid Technological Shifts
What to coverDescribe a time you had to quickly integrate a new technology, such as Agentic AI or a new GPU architecture, into an existing stable environment. Focus on your methodology for testing, validation, and minimizing disruption to ongoing workloads.