Technical practice
How would you design a generative AI application from an initial product need through implementation?
What to coverThe posting calls for designing and implementing GenAI models and applications. Suggested preparation: explain how you would define the intended use, choose an approach, establish evaluation criteria and decide what is needed before integration.
How would you improve an existing GenAI system's performance and efficiency?
What to coverThe role includes optimizing existing GenAI systems. Suggested preparation: describe how you would identify a bottleneck, compare changes against a baseline and weigh quality against latency or resource use.
How would your evaluation approach differ for NLP and computer vision work?
What to coverThe requirements name experience with both NLP and computer vision. Suggested preparation: discuss how task-specific data, error types and evaluation measures would affect model selection and testing, without assuming a particular product use case.
How would you use statistical analysis and data mining to inform an AI decision?
What to coverThe posting calls for statistical analysis, interpretation of findings and data mining experience. Suggested preparation: explain how you would assess data quality, distinguish a useful pattern from a misleading one and communicate uncertainty.
How would you structure data transformation for an AI application?
What to coverThe role involves data transformation infrastructure and calls for knowledge of data warehouses and database systems. Suggested preparation: address consistency, traceability and data quality across transformations, including how you would detect a change that affects downstream results.
How would you manage AI/ML development infrastructure and automate team tasks?
What to coverThe posting explicitly includes managing the AI/ML development process and infrastructure, automating tasks for data scientists and knowing cloud platforms. Suggested preparation: explain which repetitive steps you would prioritize and how you would judge whether the resulting process is dependable; do not assume a specific cloud provider is used.
How would you expose a model through an API for application integration?
What to coverThe role includes model APIs, API documentation and collaboration on system integration; its experience requirement also mentions frontend technologies. Suggested preparation: cover the API contract, error handling, documentation and how you would coordinate changes with an application team.
What testing protocol would you use for a GenAI system before and after deployment?
What to coverThe posting calls for testing protocols and for assessing reliability, scalability and performance. Suggested preparation: distinguish model-quality checks from system-level tests, and explain how you would identify regressions as usage or inputs change.
How would you audit a model for accuracy, ethical concerns and data privacy?
What to coverRegular model audits, ethical standards and knowledge of data privacy regulations are stated in the posting. Suggested preparation: describe the evidence you would review, how you would document limitations and how you would escalate a material concern without assuming a particular regulatory framework.
How would you decide whether an emerging GenAI technique belongs on an AI roadmap?
What to coverThe role includes exploring new techniques to enhance product capabilities and contributing to AI strategy and roadmap. Suggested preparation: compare potential benefit with evaluation results, implementation effort and operational risk, while making clear what remains unproven.
Behavioural practice
Tell me about a time you coordinated technical work across different specialties.
What to coverThe posting describes working with data scientists, programmers and other stakeholders. Suggested preparation: use a real situation, your actions to align responsibilities or resolve a dependency, and the result.
Tell me about a time you explained a complex model decision to a non-technical audience.
What to coverThe role calls for communicating model utility to product managers and executives and explaining technical concepts accessibly. Suggested preparation: describe the audience and decision, how you adapted the explanation, and what resulted.
Tell me about a difficult technical problem you diagnosed and resolved.
What to coverThe posting emphasizes problem-solving and improving AI systems. Suggested preparation: give a real situation, the evidence and actions that guided your diagnosis, and the measured result or remaining limitation.
Tell me about a time you improved a recurring development or data-science process.
What to coverThe role includes managing development processes and automating critical tasks for the data science team. Suggested preparation: explain the original problem, the change you made and its result, including any trade-off the team had to manage.
Tell me about a time you raised a concern about a model's quality or responsible use.
What to coverThe posting calls for regular audits of accuracy and ethical standards. Suggested preparation: use a real situation, explain how you communicated the concern and acted on it, and state the outcome without implying every concern had a complete resolution.