Technical practice
How would you architect an end-to-end machine learning pipeline for production use?
What to coverSuggested preparation: Walk through data ingestion, training, validation, deployment and monitoring, explaining how the stages connect. Discuss where you would add checks to make the pipeline maintainable and reliable.
How would you design an AI solution to scale?
What to coverSuggested preparation: Explain how you would estimate training and inference demands, identify bottlenecks and choose an architecture accordingly. Address trade-offs among throughput, latency, cost and operational complexity.
How would you make data preparation dependable across an ML pipeline?
What to coverSuggested preparation: Discuss data quality checks, reproducible transformations and consistency between training and production inputs. Explain how you would investigate a data issue affecting model results.
How would you choose between TensorFlow and PyTorch for a project?
What to coverSuggested preparation: Compare them against the project's development, deployment and maintenance needs rather than assuming one is always preferable. Explain how Python code quality and team familiarity would influence your choice.
What distinguishes a production-grade AI model from a successful prototype?
What to coverSuggested preparation: Cover repeatable delivery, testing, deployment controls and ongoing operation. Explain how you would decide whether a model is ready to serve real use at scale.
How would you evaluate whether a deployed model is performing well?
What to coverSuggested preparation: Describe how you would select metrics tied to the business objective and check performance across relevant data segments. Discuss how you would detect degradation and decide when corrective action is warranted.
How would you improve the reliability of a production ML solution?
What to coverSuggested preparation: Consider failure points across data, model and serving components, then explain what you would monitor and how you would respond. Distinguish model-quality failures from infrastructure or pipeline failures.
How would you assess and communicate model explainability?
What to coverSuggested preparation: Explain how the intended users and decision context would shape the explanation needed. Address the limits of an explanation method and how you would avoid presenting it as stronger evidence than it is.
How would you approach deploying an ML pipeline on a cloud platform?
What to coverSuggested preparation: Use AWS, Azure or GCP as a possible example and describe the capabilities you would need for data processing, training and serving. Discuss how scalability, reliability and cost would affect the design without assuming a specific platform is used in this role.
How would you decide whether an emerging AI technology merits adoption?
What to coverSuggested preparation: Propose a limited evaluation against an existing approach using measures relevant to performance and production operation. Explain how you would weigh potential improvement against migration effort, reliability and explainability.
Behavioural practice
Tell me about a time you identified an AI opportunity with a cross-functional team.
What to coverSuggested preparation: Describe the situation, the business need and the actions you took to assess whether AI was appropriate. State the result and how you communicated the decision to the people involved.
Tell me about a time you mentored a junior engineer on an ML engineering practice.
What to coverSuggested preparation: Explain the situation, the specific guidance you gave and how you supported the engineer's ownership of the work. Describe the outcome using evidence you can substantiate.
Tell me about a time you led a technical team through a difficult AI delivery decision.
What to coverSuggested preparation: Set out the decision, competing considerations and how you involved the team before choosing a path. Explain what you did and what happened as a result.
Tell me about a time you resolved a model-performance or reliability problem.
What to coverSuggested preparation: Describe the symptoms and impact, then explain how you investigated the cause and coordinated the response. Give the result, including how you verified the fix or reduced the risk of recurrence.
Tell me about a time you explained an AI trade-off to people outside the engineering team.
What to coverSuggested preparation: Identify the audience and decision, then describe how you made the trade-off understandable without overstating the model's capabilities. Explain the action that followed and its result.