Technical practice
Optimizing LLM Inference Latency
What to coverExplain how you would use techniques like PagedAttention, KV Cache offloading, and speculative decoding to minimize Time-to-First-Token (TTFT). Discuss the trade-offs between these methods and how you would select the appropriate serving engine, such as vLLM or TensorRT-LLM, for a specific production workload.
Designing Multi-Agent Orchestration
What to coverDescribe your experience building agentic workflows using frameworks like LangGraph or AutoGen. Focus on how you manage state, handle tool-calling precision, and ensure secure integration with enterprise databases or APIs using protocols like MCP.
MLOps Pipeline Automation
What to coverDetail your approach to building end-to-end CI/CD workflows for AI models using tools like ArgoCD and GitHub Actions. Explain how you integrate automated evaluation pipelines, such as RAGAS or DeepEval, to validate model performance and safety before deployment.
Kubernetes and Infrastructure as Code
What to coverDiscuss your experience provisioning multi-tenant infrastructure on EKS using Terraform and Helm. Explain how you manage GPU capacity and resource isolation in a shared environment to ensure high availability and cost-efficiency.
Monitoring and Troubleshooting AI Services
What to coverHow do you configure telemetry using Prometheus and Grafana to detect model drift, latency spikes, or GPU memory bottlenecks? Provide an example of how you would diagnose a production incident involving container crashes or degraded inference performance.
Hybrid Cloud AI Integration
What to coverExplain the strategy for routing workloads between on-premises GPU clusters and managed cloud services like Amazon Bedrock or SageMaker. Discuss the challenges of maintaining consistency in model versioning and artifact storage across these environments.
Function-Calling Harnesses
What to coverDescribe how you implement robust function-calling harnesses that allow LLMs to interact with enterprise data stores like AWS Aurora or DynamoDB. Focus on error handling, security, and ensuring the model correctly interprets tool schemas.
Model Quantization and Resource Management
What to coverDiscuss the role of quantization techniques in optimizing model footprint for deployment. Explain how you balance model precision against hardware constraints when managing GPU memory for high-throughput serving.
Behavioural practice
Driving Projects from Concept to Production
What to coverDescribe a time you led an AI project from initial design to production deployment. Focus on the specific technical challenges you faced, how you managed stakeholders, and the steps you took to ensure the solution met enterprise-grade requirements.
Handling Production Incidents
What to coverProvide an example of a critical failure in an MLOps pipeline or AI service you managed. Explain your diagnostic process, how you restored service to meet SLA requirements, and the post-mortem steps taken to prevent recurrence.
Leading Platform Engineering Initiatives
What to coverIf applying for the VP level, describe your experience leading a team to architect an enterprise AI platform. Discuss how you set technical standards, mentored team members, and balanced immediate delivery needs with long-term platform scalability.
Managing Conflicting Priorities in AI Development
What to coverDescribe a situation where you had to balance the need for rapid AI model iteration with the requirement for strict production stability and security. How did you negotiate these trade-offs with your team or business partners?