Technical practice
How would you turn a business problem into a testable AI hypothesis?
What to coverThe role involves defining requirements with cross-functional teams and testing hypotheses with data. Suggested preparation: explain how you would choose a measurable outcome, a baseline and a test that connects model results to a business decision.
When would you consider GPT, a VAE or a GAN for a generative AI task?
What to coverThe posting names all three approaches and expects experience implementing generative AI models. Suggested preparation: compare what each generates, its training or inference trade-offs, and how you would justify a choice for a stated use case.
How would you structure a Python implementation of a generative model?
What to coverThe posting calls for Python, experience with TensorFlow, PyTorch or Keras, and sound software engineering principles. Suggested preparation: describe separable data, training and inference components, along with tests and reproducible experiments.
How would you diagnose a generative model that is too slow or costly at scale?
What to coverThe role includes improving model performance, scalability and efficiency. Suggested preparation: identify what you would measure first, then discuss trade-offs among model quality, latency, throughput and resource use.
How would you design and evaluate a prompt strategy?
What to coverThe role includes leading prompt engineering strategies and choosing effective prompt generation approaches with researchers and developers. Suggested preparation: explain how you would compare prompt variants against defined output criteria and investigate inconsistent responses.
How could NLP techniques improve a GenAI application's outputs?
What to coverThe posting calls for NLP experience and names SpaCy, NLTK and Hugging Face as example tools. Suggested preparation: discuss a relevant preprocessing or analysis technique, how you would check its effect on outputs, and when added complexity would not be worthwhile.
How would you prepare data from varied sources for a speech or voice application?
What to coverThe role calls for data quality and accuracy across formats and sources in omnichannel conversational AI. Suggested preparation: describe checks for consistency, missing or incorrect data, and how you would trace a downstream quality issue to its source.
What would make an AI application pipeline production-ready in cloud or on-premises settings?
What to coverThe project role describes production-ready AI systems using cloud AI services or on-premises pipelines; the skills list names AWS, GCP and Azure as examples. Suggested preparation: discuss deployment boundaries, testing, monitoring and the trade-offs that would guide a cloud-versus-on-premises choice.
How would you evaluate whether a model improvement supports a business decision?
What to coverThe role connects generative AI insights to decisions and also calls for statistical modeling and data visualization familiarity. Suggested preparation: explain how you would compare against a baseline, assess uncertainty and present results without overstating their implications.
How would you assess a new generative AI technique for use in an existing product?
What to coverThe posting includes researching advances and identifying opportunities to integrate them into products or services. Suggested preparation: outline a limited evaluation against current performance, implementation effort and operational constraints before recommending adoption.
Behavioural practice
Tell me about aligning an AI project with stakeholders who had different priorities.
What to coverThe role requires cross-functional requirements work and alignment with business goals. Suggested practice: use a real situation, your actions to clarify priorities and a measurable or observable result.
Describe a time you led a technical approach while incorporating others' input.
What to coverThe posting includes leading prompt strategy design and collaborating with researchers and developers. Suggested practice: give a real situation, how you evaluated input and made decisions, and the result.
Tell me about finding a data-quality problem that affected an AI outcome.
What to coverThe role explicitly calls for data quality and accuracy. Suggested practice: describe the situation, how you investigated and addressed it, and what changed as a result.
Describe a time you explained a complex AI concept to a non-technical audience.
What to coverThe posting calls for technical specifications, user guides or presentations and communication with diverse audiences. Suggested practice: explain the audience and situation, how you adapted the explanation, and the resulting decision or understanding.
Tell me about balancing independent progress with collaboration on a changing project.
What to coverThe posting mentions independent and collaborative work in a dynamic environment, plus familiarity with Agile or Scrum. Suggested practice: use a real situation, the actions you took to coordinate and adapt, and the outcome.