Technical practice
How would you turn an insurance-domain business need into a Data and AI architecture roadmap?
What to coverThe posting calls for translating business requirements into technical solutions and roadmaps. Suggested preparation (inference): explain how you would clarify the desired outcome, data availability, constraints and success measures before sequencing a production-ready design.
How would you design an end-to-end data and AI solution for production?
What to coverThe role leads end-to-end architectures and requires knowledge of data integration and modern data platforms. Suggested preparation (inference): walk through data flow, service boundaries, reliability and scaling trade-offs, including how you would identify a failing component.
How would you evaluate and scale an enterprise Generative AI solution?
What to coverBuilding and scaling GenAI solutions is an explicit responsibility. Suggested preparation (inference): discuss quality evaluation, latency, cost and operational reliability, and how those findings would affect the architecture.
When would an agentic AI approach be appropriate, and how would you control it?
What to coverThe posting specifically mentions building agentic AI solutions and hands-on framework expertise. Suggested preparation (inference): compare agentic and simpler approaches, then explain boundaries on actions, monitoring and failure handling.
How would you integrate an AI capability across existing systems and APIs?
What to coverThe role drives integration patterns across systems, platforms and APIs and calls for microservices knowledge. Suggested preparation (inference): cover interface contracts, data flow, security and how you would troubleshoot an integration failure.
What trade-offs would guide a scalable cloud-based Data and AI architecture?
What to coverThe posting seeks strong AWS proficiency and cloud architecture skills across AWS, Azure or GCP. Suggested preparation (inference): explain how you would weigh scalability, resilience, security and cost without assuming the employer uses a particular service.
How would you operationalize and monitor an AI solution after deployment?
What to coverExperience with MLOps or LLMOps is stated in the posting. Suggested preparation (inference): describe versioning, release checks, monitoring and rollback, including what signals would prompt investigation.
How would you choose and orchestrate AI/ML tools and libraries?
What to coverThe posting calls for proficiency in integrating and orchestrating AI/ML tools and libraries. Suggested preparation (inference): explain selection criteria, component interfaces and how you would isolate errors across a workflow.
How would you evaluate an NLP application such as sentiment analysis or article summarization?
What to coverBoth applications appear in the posting as examples of NLP use cases. Suggested preparation (inference): compare task-appropriate quality measures, review representative errors and explain how business needs would shape evaluation.
How would you incorporate governance, security and responsible AI into a solution design?
What to coverThe role establishes practices in these areas and addresses business and regulatory compliance risks. Suggested preparation (inference): discuss risk identification, controls, review points and how you would respond when a new threat changes the design.
Behavioural practice
Tell me about a time you aligned teams around a technical architecture.
What to coverThe role involves cross-functional delivery and influencing teams. Suggested preparation (inference): use a real situation, describe how you handled differing priorities, and state the decision and result.
Tell me about a time you mentored an engineer through a difficult problem.
What to coverMentoring engineers and growing the Data & AI CoE capability are stated responsibilities. Suggested preparation (inference): give the situation, your specific coaching actions and the observable result without taking credit for someone else's work.
Tell me about a time you identified and mitigated a governance or compliance risk.
What to coverThe posting emphasizes accountability for business and regulatory compliance risks. Suggested preparation (inference): explain the situation, how you raised and addressed the concern, and what changed as a result.
Tell me about a time you took initiative to improve a solution or way of working.
What to coverThe posting asks for initiative, ownership and willingness to embrace change. Suggested preparation (inference): describe the original problem, the change you led and its measurable or observable result.
Tell me about a time an emerging technology or threat changed your technical recommendation.
What to coverThe role calls for keeping abreast of emerging technologies and threats and highlighting potential risks. Suggested preparation (inference): use a real situation, explain how you assessed the new information, and describe your recommendation and its result.