Technical practice
Designing Event-Driven AI Workflows
What to coverExplain how you would architect an event-driven system to orchestrate AI agents for marketing workflows. Discuss your approach to decoupling services, handling asynchronous message processing, and ensuring reliability when integrating LLM-based tasks.
Productionizing LLM Applications
What to coverDescribe the challenges of moving an LLM-based application from prototype to production. Focus on strategies for prompt management, latency optimization, cost control, and implementing robust error handling for tool-calling failures.
RAG and Vector Database Implementation
What to coverDiscuss the trade-offs between different vector database architectures for semantic search. Explain how you ensure data privacy and security when indexing enterprise data for RAG pipelines.
Scalable Full-Stack AI Integration
What to coverHow do you ensure a React-based frontend remains performant when interacting with complex backend AI agent workflows? Address state management, streaming responses from LLMs, and maintaining user experience during long-running processes.
Kubernetes for AI Workloads
What to coverExplain how you would configure Kubernetes to support scalable AI services. Discuss resource management, such as GPU scheduling or horizontal pod autoscaling, and how you ensure observability for distributed AI components.
Tool Calling and Agent Autonomy
What to coverWhen building AI agents, how do you define the boundaries of tool usage to prevent hallucinations or unauthorized actions? Discuss your approach to validation, logging, and human-in-the-loop checkpoints.
Data Governance in AI Pipelines
What to coverGiven the focus on enterprise security, how do you handle data governance when feeding proprietary marketing data into LLMs? Explain your approach to PII redaction, data lineage, and compliance with privacy standards.
System Reliability and Observability
What to coverWhat metrics are critical for monitoring the health of a production AI system? Describe how you would implement tracing and logging to debug non-deterministic outputs from LLMs in a distributed environment.
Behavioural practice
Owning Technical Initiatives
What to coverDescribe a time you owned a complex technical project from design to production. Focus on how you managed cross-functional dependencies, handled unexpected technical blockers, and ensured the final delivery met business requirements.
Collaborating with Non-Technical Stakeholders
What to coverProvide an example of how you translated complex AI capabilities into actionable solutions for marketing or GTM teams. Explain how you balanced technical feasibility with the specific needs of the business users.
Handling Technical Disagreement
What to coverTell me about a time you disagreed with a team member or stakeholder regarding an architectural decision. How did you present your case, what data did you use to support your position, and what was the final outcome?
Driving Process Automation
What to coverDescribe a situation where you identified a manual, inefficient process and automated it. What was the impact on developer productivity or system reliability, and how did you ensure the new solution was maintainable?