Technical practice
How would you identify a useful AI augmentation opportunity in an existing workflow?
What to coverThe role involves understanding teams’ workflows before building. Suggested preparation (inferred): explain how you would map the task, compare an AI approach with simpler alternatives, and define what a useful result would look like.
How would you design an agent-based workflow for a knowledge or analysis task?
What to coverThe posting specifically names agent-based workflows and orchestration. Suggested preparation (inferred): describe how you would divide responsibilities, manage intermediate results, and prevent failures in one step from silently affecting the final output.
When should an agent escalate to a person, and what should that handoff contain?
What to coverEscalation and human-agent interaction are stated responsibilities. Suggested preparation (inferred): discuss triggers such as uncertainty or consequential actions, and how to give a reviewer enough context to make a decision.
How would you evaluate whether an agentic system is reliable enough for its intended use?
What to coverThe role calls for production systems people depend on and an understanding of where AI tools break down. Suggested preparation (inferred): cover representative task tests, failure analysis, and the trade-off between automation and human review.
How would you integrate an AI capability with existing services through an MCP server or API?
What to coverMCP servers, APIs, and existing infrastructure are named in the posting. Suggested preparation (inferred): explain interface boundaries, error handling, and how you would keep an integration maintainable as either side changes.
What would you consider when connecting an AI workflow to an existing data pipeline?
What to coverData-pipeline integration is an advertised responsibility. Suggested preparation (inferred): address data freshness, input quality, failure recovery, and how downstream users would know when results are incomplete.
How would you turn a working AI prototype into a dependable production system?
What to coverThe team’s stated focus is shipping systems that others rely on, not conducting research. Suggested preparation (inferred): discuss testing, predictable failure behavior, maintainability, and the trade-offs you would resolve before deployment.
How would you monitor and troubleshoot a deployed AI-powered workflow?
What to coverMonitoring, observability, and operational support are explicit duties. Suggested preparation (inferred): describe what you would observe across the workflow, how you would isolate a failure, and how findings would inform a fix.
How would your deployment approach differ for a short integration and a multi-month platform build?
What to coverThe posting says projects can range from a two-day integration to a multi-month build. Suggested preparation (inferred): compare the minimum safeguards needed to ship quickly with the deployment and maintenance choices justified by a longer-lived system.
How would you decide whether a solution belongs in a shared AI platform?
What to coverThe role contributes to the platform through production work across diverse domains. Suggested preparation (inferred): explain how you would distinguish a reusable pattern from a domain-specific need, including the costs of premature abstraction.
Behavioural practice
Tell me about a time you understood another team’s problem before proposing a solution.
What to coverWorking directly with partner teams and understanding their problems are central to the role. Suggested preparation (inferred): use a real situation, the questions and actions you took to clarify the workflow, and the resulting decision or outcome.
Describe a project you scoped and delivered with little initial specification.
What to coverThe posting calls for autonomy in scoping, architecture, and delivery. Suggested preparation (inferred): give the situation, how you bounded the problem and made decisions, and the result.
Tell me about a time you switched between projects or unfamiliar domains.
What to coverRapid context-switching across projects, domains, and partner teams is explicitly mentioned. Suggested preparation (inferred): describe the situation, how you learned what mattered and managed competing demands, and what happened.
Describe a production issue you helped resolve in a system others depended on.
What to coverThe role includes operational support for dependable production systems. Suggested preparation (inferred): explain the situation, your diagnostic and communication actions, and the result, including what changed afterward.
Tell me about a time you had to explain an architectural trade-off to collaborators.
What to coverArchitectural decisions and strong communication are stated expectations. Suggested preparation (inferred): present the situation, how you made the options and consequences understandable, and the decision or result.