Technical practice
How would you structure a maintainable Python implementation of an AI feature?
What to coverSuggested preparation: Discuss clear interfaces, tests, error handling, and readable code, drawing on the posting’s emphasis on Python, computer science fundamentals, and coding practices. Explain how you would keep data processing, model logic, and application code separate.
How would you use Pandas and NumPy to prepare data for an ML solution?
What to coverSuggested preparation: Walk through validation, missing or inconsistent values, transformations, and checks on the resulting data. Explain when vectorized operations are useful and how you would verify that a pipeline produces the intended inputs.
How would you prototype and evaluate a solution using scikit-learn?
What to coverSuggested preparation: Describe choosing a baseline, separating training from evaluation data, and selecting measures appropriate to the problem. Explain how you would compare results before deciding whether an approach is worth taking further.
How would you improve a prompt that produces inconsistent outputs?
What to coverSuggested preparation: Discuss clarifying the task and output format, testing representative inputs, and comparing revisions against observable failure cases. Distinguish prompt changes from problems that require better context or application logic.
How would you design and evaluate a retrieval-augmented generation workflow?
What to coverSuggested preparation: Explain document preparation, retrieval, use of retrieved context in a response, and checks for relevance and unsupported answers. Discuss how retrieval quality and generation quality can fail independently.
When would an agentic workflow be preferable to a simpler fixed workflow?
What to coverSuggested preparation: Compare flexibility with predictability, latency, and the difficulty of testing multi-step behavior. Explain how you would constrain actions and inspect intermediate steps when troubleshooting.
How would you integrate a conventional database and a vector database in an AI application?
What to coverSuggested preparation: Explain what information each would hold, how records and retrieved content would stay linked, and how updates would be handled. The posting names databases and vector databases but does not prescribe a particular product or architecture.
How would you diagnose incorrect or stale data flowing through an AI data pipeline?
What to coverSuggested preparation: Trace inputs, transformations, storage, and downstream outputs to isolate where the discrepancy first appears. Discuss validation and repeatable checks that would help prevent a recurrence.
What would you check before moving an AI prototype into production?
What to coverSuggested preparation: Compare prototype results with the product need, then discuss testing, failure handling, reproducibility, and operational readiness. Explain trade-offs between shipping quickly and resolving risks exposed during experimentation.
How would you containerize and deploy an AI application using Docker or a similar tool?
What to coverSuggested preparation: Cover dependencies, configuration, a repeatable build, and a way to verify the application runs as expected after deployment. Explain how you would investigate a failure that occurs in the container but not locally.
Behavioural practice
Tell me about a time you worked with another function to deliver a technical product feature.
What to coverSuggested preparation: Use a real situation to explain the differing needs, the actions you took to reach a workable decision, and the result. The posting specifically calls for cross-functional delivery of AI-powered products.
Tell me about a time you helped shape a technical direction with senior stakeholders.
What to coverSuggested preparation: Describe the situation, the options and evidence you presented, your actions in discussing trade-offs, and the outcome. Keep your own contribution distinct from the group’s decision.
Tell me about a time you mentored an intern or junior engineer.
What to coverSuggested preparation: Give a real situation, describe how you adapted your guidance and feedback, and explain the result for their work or learning. The posting explicitly includes mentoring interns and junior engineers.
Tell me about a time you had to turn an early technical experiment into something usable.
What to coverSuggested preparation: Explain the initial uncertainty, the actions you took to test and refine the approach, and the result. Focus on how you decided what was ready to advance and what still needed work.
Tell me about a difficult engineering problem you diagnosed under time pressure.
What to coverSuggested preparation: Use a real example to describe the symptoms, how you narrowed down the cause, what you did, and the outcome. If relevant, explain how you balanced a prompt fix with a more durable solution; a fast-paced product environment is listed as a plus, not a requirement.