Technical practice
Designing Scalable Enterprise AI Platforms
What to coverExplain your approach to architecting a platform like the Central AI Kitchen (CAK) that supports diverse enterprise needs. Focus on how you balance modularity, scalability, and the integration of cloud-native technologies with existing enterprise infrastructure.
Model Lifecycle Management and Governance
What to coverDescribe the architectural components required to manage the full lifecycle of AI models, from selection and fine-tuning to deployment and retirement. Address how you implement versioning, regression testing, and performance monitoring within a regulated environment.
Implementing RAG and Agentic AI Architectures
What to coverDiscuss the design patterns for integrating Retrieval-Augmented Generation (RAG) and agentic workflows into a secure enterprise platform. Highlight how you handle data context, orchestration, and the Model Context Protocol (MCP) to ensure reliability and accuracy.
Evaluating Foundation Models for Enterprise Use
What to coverWhat criteria do you use to benchmark and select between hosted, self-hosted, and open-weight foundation models? Explain how you weigh factors like cost, latency, data privacy, and model performance against specific business requirements.
AI Security and Responsible AI Integration
What to coverHow do you embed security and responsible AI guardrails directly into the platform architecture? Discuss your approach to data governance, privacy compliance, and mitigating risks such as prompt injection or model bias.
Multimodal AI Infrastructure
What to coverWhat are the unique architectural challenges when scaling a platform to support multimodal models, including vision, speech, and video? Explain how you handle the increased computational demands and data processing requirements.
Observability and Reliability in AI Services
What to coverDescribe the operational architecture needed to ensure high availability and observability for production AI services. Focus on how you monitor model drift, latency, and resource utilization in a containerized, distributed environment.
Build vs. Buy Strategy for AI Capabilities
What to coverWhen presented with a new AI requirement, how do you determine whether to build a custom solution or integrate a commercial product? Provide a framework for assessing total cost of ownership, alignment with long-term strategy, and technical feasibility.
Behavioural practice
Influencing Senior Stakeholders and Governance Boards
What to coverDescribe a time you had to present a complex architecture proposal to a review board or senior leadership. Explain how you simplified the technical details to gain buy-in while addressing concerns regarding risk, cost, and alignment with enterprise standards.
Cross-Functional Collaboration
What to coverProvide an example of how you successfully collaborated with Cybersecurity, Enterprise Architecture, and Engineering teams to deliver a platform initiative. How did you resolve conflicting priorities or requirements between these groups?
Mentoring and Technical Leadership
What to coverShare an experience where you mentored junior architects or engineers to improve their technical output or architectural thinking. What was your approach to fostering a culture of innovation and excellence within the team?
Navigating Technical Debt and Innovation
What to coverTell us about a situation where you had to balance the need for rapid AI innovation with the constraints of legacy enterprise systems. How did you manage the trade-offs to ensure the platform remained future-ready?