Technical practice
How would you design retrieval for an enterprise AI feature?
What to coverThe role includes building AI systems involving retrieval. Suggested preparation: explain how you would select relevant source material, handle weak or missing matches, and balance answer quality against latency.
How would you build and maintain a vector-based retrieval workflow?
What to coverRAG and vector workflows are preferred experience in the posting. Suggested preparation: discuss how documents become searchable, how you would keep the index current, and how you would test whether retrieved results are useful.
How would you structure prompts across a multi-step workflow?
What to coverThe responsibilities explicitly include prompt orchestration and workflow integration. Suggested preparation: describe how you would manage context between steps, handle unreliable outputs, and keep prompts maintainable as the workflow changes.
How would you expose an LLM-backed feature through a product API?
What to coverThe role calls for model integration, strong API practices, and scalable service integration; FastAPI is also highlighted. Suggested preparation: explain request and response design, error handling, and how you would prevent a slow model call from degrading the wider service.
How would you trace data through an AI feature integrated with existing services?
What to coverStrong knowledge of data flow and scalable service integration is a minimum qualification. Suggested preparation: walk through inputs, transformations, model calls, and outputs, noting where validation and failure handling belong.
How would you integrate an intelligent feature into an enterprise workflow?
What to coverThe posting emphasizes features that remain practical within enterprise software. Suggested preparation: explain how you would define the feature’s boundaries, connect it to existing workflows, and decide when a simpler non-AI approach would be more useful.
How would you evaluate whether a retrieval-based feature is improving?
What to coverModel evaluation is listed as preferred exposure, and the role aims to improve feature quality and usefulness. Suggested preparation: distinguish retrieval quality from final-answer quality, then describe how you would compare changes using representative cases.
What would you observe in a production AI workflow?
What to coverThe responsibilities call for observable, production-aware AI engineering. Suggested preparation: identify signals for latency, failures, and output quality, and explain how you would use them to locate a problem without collecting unnecessary sensitive data.
How would you address security when connecting models to enterprise data?
What to coverSecure AI engineering is an explicit responsibility. Suggested preparation: discuss access boundaries, treatment of sensitive inputs and outputs, and the trade-off between useful context and unnecessary data exposure.
How would you prepare an AI integration for sustained production use?
What to coverThe role emphasizes scalable products, maintainability, and productionization; cloud-hosted AI delivery is preferred knowledge. Suggested preparation: explain how you would manage changes, recover from service failures, and keep the integration understandable to other engineers.
Behavioural practice
Tell me about a time you worked with product and platform teams to improve a feature.
What to coverCollaboration with both teams is a stated responsibility. Suggested preparation: use a real situation, describe your actions in resolving different priorities, and state the result for usefulness or maintainability.
Tell me about a time you delivered technical work under a structured engineering process.
What to coverThe posting emphasizes structured delivery and accountability. Suggested preparation: describe the situation, how you made decisions and progress clear, and the result without claiming that a particular process is used at Willsfly.
Tell me about a time you improved the operational quality of a software feature.
What to coverThe role calls for observable, production-aware engineering. Suggested preparation: give a real situation, the diagnostic or preventive action you took, and the result for reliability or issue detection.
Tell me about a time you chose maintainability over a faster short-term solution.
What to coverThe posting highlights product maturity and long-term software continuity. Suggested preparation: explain the situation and trade-off, what you did, and the result for future changes or support.
Tell me about a time you coordinated engineering work across locations or teams.
What to coverThe role operates within a connected U.S. and India product engineering structure. Suggested preparation: use a real situation to show how you clarified ownership or handoffs, what actions you took, and the delivery result.