Technical practice
Designing Scalable AI Agentic Systems
What to coverExplain your approach to architecting agentic systems that remain performant under high-concurrency enterprise loads. Discuss how you handle state management, tool-use orchestration, and error recovery in autonomous workflows.
Optimizing LLM Performance in Production
What to coverDescribe your methodology for evaluating and improving LLM outputs in a production environment. Focus on techniques like prompt engineering, RAG (Retrieval-Augmented Generation) optimization, and implementing guardrails to ensure reliability.
MLOps and CI/CD for AI Services
What to coverHow do you integrate AI model lifecycle management into standard DevOps pipelines? Address strategies for automated testing of AI components, model versioning, and continuous monitoring of model drift in cloud-hosted services.
Building Cloud-Native Applications on Azure
What to coverDiscuss your experience leveraging Azure services like App Service or Service Fabric for scalable SaaS applications. Explain how you manage containerization with Docker and ensure high availability for global enterprise users.
Frontend Architecture with React and TypeScript
What to coverDetail your experience in building and maintaining scalable UX component libraries. Explain how you ensure performance, accessibility, and consistency across complex enterprise web applications.
Integrating AI into Dynamics 365 or Power Platform
What to coverIf you have experience with the Power Platform or Dynamics 365, describe how you have extended these platforms with custom AI capabilities. If not, explain how you would approach integrating an external AI service into a pre-existing enterprise business application.
Handling Data and State in Distributed Systems
What to coverExplain your strategies for managing data consistency and database interactions in a distributed, high-scale environment. Discuss trade-offs between different database querying approaches and how they impact application latency.
Debugging and Troubleshooting Complex Services
What to coverDescribe your process for diagnosing performance bottlenecks or reliability issues in a 24/7 production environment. Mention specific tools or telemetry strategies you use to isolate issues between the application layer and the AI model layer.
Behavioural practice
Cross-Functional Collaboration with Non-Technical Stakeholders
What to coverDescribe a situation where you had to translate complex AI or technical requirements to product managers or UX designers. Explain how you ensured alignment on feature scope and technical feasibility.
Mentoring and Supporting Team Growth
What to coverProvide an example of how you have supported less experienced team members in their technical development. Focus on how you balanced your own delivery responsibilities with the need to provide effective guidance.
Navigating Ambiguity in Feature Design
What to coverTell me about a time you were tasked with envisioning a new feature with limited initial requirements. How did you collaborate with architects and stakeholders to define the design and implementation path?
Managing Technical Debt vs. Innovation
What to coverDescribe a scenario where you had to balance the need for rapid feature delivery with the necessity of maintaining high engineering standards. How did you communicate the trade-offs to your team or leadership?
Adapting to New Technologies
What to coverGive an example of a time you had to quickly learn and implement a new technology or framework to solve a business problem. How did you assess its suitability and integrate it into your existing workflow?