Technical practice
How would you design an AI-native Software Development Life Cycle (SDLC) framework for a large enterprise?
What to coverCandidates should discuss how artificial intelligence can be integrated across each phase of the SDLC, from requirements gathering and architecture design to automated coding, testing, and security. Address how to balance productivity gains with quality assurance and risk management.
What metrics would you establish to measure engineering productivity and value realization from AI tools?
What to coverA strong answer should propose both quantitative metrics (such as cycle time reduction, code acceptance rates, and defect density) and qualitative measures. Explain how to tie these metrics directly to broader enterprise value and C-suite objectives.
How do you translate an ambiguous enterprise AI transformation ambition into an executable roadmap?
What to coverFocus on breaking down high-level strategy into structured, phased milestones. Discuss how to identify high-impact use cases, define data and tooling requirements, and establish clear ownership across client stakeholder groups.
What platform and tooling strategies are essential when scaling AI-assisted software delivery across distributed engineering teams?
What to coverAddress the selection criteria for code generation assistants, automated testing frameworks, and secure deployment pipelines. Emphasize the importance of governance, scalability, and integration with existing enterprise toolchains.
How would you manage delivery operating model changes when introducing AI agents into existing enterprise workflows?
What to coverDiscuss organizational change management principles, including how to redefine team roles (such as workstream owners and agent hierarchies), address potential user resistance, and realign incentives to foster an AI-native culture.
How do you approach securing AI-generated code and managing risks in an enterprise environment?
What to coverHighlight the integration of automated vulnerability scanning, compliance checks, and security guardrails directly into the AI-native CI/CD pipeline. Address intellectual property and data privacy considerations.
Behavioural practice
Describe a situation where you had to present a complex technology strategy to C-suite stakeholders like a CIO or CTO.
What to coverUse the Situation, Action, Result framework to explain how you prepared for the meeting, tailored your communication to executive priorities, handled challenging questions or skepticism, and secured their buy-in.
Tell me about a time when you owned a complex workstream end-to-end as the primary day-to-day client contact.
What to coverShare an example highlighting your project management skills, how you maintained transparent communication with the client, and how you managed shifting requirements or delivery hurdles to achieve a successful outcome.
Give an example of how you bridged the gap between business transformation ambition and technical execution.
What to coverDescribe a scenario where stakeholders had high-level goals without clear execution paths. Explain how you translated those goals into concrete deliverables, scoped the initiative, and guided the technical team toward measurable results.
Describe a time when you drove an organizational or operating model change within an engineering team.
What to coverDetail the specific change management strategies you employed, how you addressed team pushback or operational friction, and how you measured the success and adoption rate of the new model.