Technical practice
How would you design a production-ready application that uses a generative AI model?
What to coverThe role calls for production-ready AI applications and cloud or on-premises pipelines. Suggested preparation: explain the application components, how the model fits into the pipeline, and trade-offs affecting reliability, performance and maintainability.
How would you decide whether an LLM is appropriate for a proposed AI feature?
What to coverGenerative AI is required, while LLM experience is listed as good to have. Suggested preparation: discuss the task, expected output quality, limitations and how you would compare an LLM-based approach with alternatives.
How would you integrate an AI capability into an existing system?
What to coverThe posting explicitly calls for integration into existing systems. Suggested preparation: describe how you would establish interface requirements with other teams, test the integration and handle failures without disrupting existing functionality.
How would you evaluate and improve an existing AI model or system?
What to coverContinuous evaluation and improvement are stated responsibilities. Suggested preparation: explain how you would establish a baseline, choose measures tied to the system’s purpose, examine errors and verify that a change improves results.
How would you balance AI performance with user experience?
What to coverThe role includes optimizing performance and enhancing user experience. Suggested preparation: discuss trade-offs such as response time versus output quality, and how user feedback and system measurements would inform decisions.
What would you consider when deploying an AI application using cloud AI services?
What to coverThe posting asks for experience developing and deploying AI applications in cloud environments. Suggested preparation: explain deployment boundaries, service dependencies, monitoring and how you would assess whether a cloud service fits the application pipeline.
How would you use TensorFlow or PyTorch to investigate a model-training problem?
What to coverA strong understanding of these deep learning frameworks is listed. Suggested preparation: walk through how you would inspect data, model behaviour and training results to isolate a problem, without assuming a particular framework is required.
How do preprocessing and augmentation choices affect model training?
What to coverThe posting mentions familiarity with both techniques. Suggested preparation: explain how you would check data quality, select transformations appropriate to the data and test whether augmentation helps rather than distorts the training objective.
How would you assess a chatbot or image-processing use case before implementation?
What to coverChatbots and image processing are examples of solutions the role could involve, not stated assignments. Suggested preparation: choose one example and discuss its inputs, success criteria, likely failure cases and whether an AI approach is warranted.
How would you troubleshoot a deployed AI application whose results have deteriorated?
What to coverThe role combines production-ready systems with ongoing evaluation and improvement. Suggested preparation: explain how you would distinguish data, model, integration and pipeline issues, then validate a fix against the established baseline.
Behavioural practice
Tell me about a consequential technical decision you made with a team.
What to coverThe role includes responsibility for team decisions. Suggested preparation: give a real situation, your decision-making actions and the result, including how you handled competing technical views.
Describe a time you coordinated with other teams to integrate a technical solution.
What to coverThe posting calls for cross-functional collaboration and contributions to decisions across teams. Suggested preparation: use a real situation, your actions to resolve dependencies or disagreements, and the resulting outcome.
Tell me about a time you helped a junior colleague develop.
What to coverMentoring junior team members is an explicit responsibility. Suggested preparation: describe the colleague’s situation, the specific support you provided and the observable result without overstating your contribution.
Describe a problem you solved that affected more than your immediate team.
What to coverThe role calls for solutions to problems within and across teams. Suggested preparation: explain the situation, how you diagnosed and coordinated the response, and the result for those affected.
Tell me about a time evidence led you to improve an existing technical system.
What to coverContinuous evaluation and improvement of AI models and systems are stated duties. Suggested preparation: give a real situation, the evidence and actions behind the change, and the measured or observed result.