Technical practice
How would you choose among a GPT-based model, a VAE, and a GAN for a generative AI use case?
What to coverThe posting names all three approaches. Compare the type of output needed, available data, evaluation methods, and implementation trade-offs rather than treating the models as interchangeable.
How would you take a generative AI application from a prototype to a production-ready cloud or on-premises pipeline?
What to coverThe role calls for production-ready applications using AI tools and cloud AI services. Suggested preparation: explain pipeline components, testing, monitoring, and deployment trade-offs without assuming a particular platform or architecture.
How would you improve an existing generative AI model's performance, scalability, and efficiency?
What to coverExplain how you would establish a baseline, identify bottlenecks, and measure whether changes improve the relevant outcomes. Discuss trade-offs between output quality, resource use, and response time.
How would you design and evaluate a prompt-generation strategy?
What to coverThe posting specifically calls for leading prompt engineering and determining effective prompt-generation approaches. Describe a repeatable way to compare prompts, examine model outputs, and collaborate with researchers and developers on revisions.
How would you use NLP techniques to improve generative AI output?
What to coverConnect a specific NLP task, such as text preprocessing or entity handling, to the output problem it addresses. Explain how you would test its contribution; the posting names spaCy, NLTK, and Hugging Face as examples, not a mandated stack.
How would you detect and address data-quality problems before using data for an AI model?
What to coverThe role explicitly calls for data quality and accuracy. Suggested preparation: discuss checks for missing, inconsistent, or unsuitable data and how you would verify that corrective steps improve the dataset.
What challenges would you examine when handling multiple data formats and sources for a speech or voice application?
What to coverThe posting mentions omni-channel speech and voice data within conversational AI. Suggested preparation: explain how you would assess format compatibility, source consistency, and the effect of data issues on downstream processing without assuming a specific channel or service.
How would you turn a business problem into a testable data-driven hypothesis?
What to coverDescribe how you would define the question, choose an appropriate statistical approach, and interpret results for a business decision. The role also names visualization tools, so explain how a clear visual could communicate findings without overstating what the data shows.
How would you structure and test a Python implementation of a deep learning model?
What to coverThe posting calls for Python, frameworks such as PyTorch, TensorFlow, or Keras, and sound software engineering principles. Discuss modularity, data handling, reproducible tests, and the trade-offs you would consider when selecting a framework.
How would you assess whether a recent generative AI technique is worth integrating into an existing product or service?
What to coverThe role includes researching advances and identifying integration opportunities. Suggested preparation: describe how you would compare evidence of improved capability with implementation effort, operational cost, and fit for the stated business objective.
Behavioural practice
Tell me about a time you aligned an AI project's requirements with a business problem.
What to coverUse a real example to describe the situation, how you worked with different stakeholders to define the objective, and the result. Make your own contribution to resolving differing priorities clear.
Describe a time you led a technical approach while collaborating with researchers or developers.
What to coverExplain the situation, the approach you proposed, how you incorporated others' input, and the result. A prompt-engineering example would be especially relevant if you have one, but is not necessary.
Tell me about a time you explained a complex AI concept to a non-technical audience.
What to coverDescribe the audience and decision at stake, the documentation or presentation choices you made, and the result. Show how you kept the explanation accurate while making it useful.
Describe a time you independently investigated a difficult model or data problem.
What to coverSet out the situation, your analytical steps, when you sought collaboration, and what the investigation achieved. Focus on evidence used to diagnose the problem rather than claiming a particular outcome.
Tell me about a time you helped establish or improve a development practice.
What to coverThe posting mentions contributing to generative AI standards and familiarity with Agile or Scrum. Describe a real situation, the change you helped make, how colleagues adopted it, and the result.