Technical practice
Optimizing GPU and InfiniBand Performance
What to coverExplain how you identify and resolve bottlenecks in high-performance AI infrastructure. Focus on metrics for GPU utilization, InfiniBand fabric congestion, and how you correlate these with application-level performance in distributed training workloads.
Scaling Kubernetes for AI Workloads
What to coverDescribe your approach to managing containerized AI environments at scale. Discuss strategies for resource scheduling, handling pod preemption, and ensuring high availability for stateful AI services within a Kubernetes cluster.
Root Cause Analysis in Distributed Systems
What to coverDetail your methodology for conducting a blameless post-mortem after a major service outage. Emphasize how you move from initial incident response to identifying systemic failures and implementing long-term reliability improvements.
Infrastructure Automation and Predictive Analysis
What to coverDiscuss how you design automation tools to manage large-scale cloud infrastructure. Explain how you incorporate predictive analysis into monitoring to proactively address potential failures before they impact service availability.
Managing Reliability for Generative AI
What to coverWhat unique reliability challenges do generative AI workloads present compared to traditional cloud services? Address considerations for compute-intensive tasks, long-running jobs, and the impact of hardware failures on model training.
Network Latency in HPC Environments
What to coverHow do you troubleshoot network latency issues in an InfiniBand-based HPC environment? Describe the tools and techniques you use to isolate issues between the application, the network fabric, and the underlying storage layer.
Security in AI Infrastructure
What to coverHow do you ensure the security of AI infrastructure while maintaining high performance? Discuss your approach to securing containerized environments, managing access to sensitive AI models, and implementing infrastructure-as-code security practices.
Cloud Platform Reliability Strategy
What to coverGiven your experience with Azure or similar cloud platforms, how do you balance the trade-offs between rapid feature deployment and system stability? Explain your strategy for implementing service level objectives (SLOs) and error budgets.
Behavioural practice
Leading Through Technical Ambiguity
What to coverDescribe a situation where you had to provide technical leadership on a complex AI infrastructure project with unclear requirements. Explain how you aligned cross-functional teams and defined a clear path forward.
Acting as a Customer Advocate
What to coverProvide an example of a time you advocated for a customer's needs when they conflicted with internal engineering priorities. How did you balance the customer's requirements for service excellence with the technical constraints of the platform?
Handling High-Pressure Incident Response
What to coverTell me about a time you led the response to a critical production incident. Describe your role in coordinating the team, communicating with stakeholders, and ensuring the service was restored while maintaining safety and accuracy.
Driving Innovation in Infrastructure
What to coverDescribe a time you identified an emerging technology or industry trend and successfully recommended its adoption to improve infrastructure reliability or performance. What was the process for evaluating the risk and impact?