Technical practice
How would you design instruction fine-tuning for a specific business context?
What to coverThe role explicitly emphasizes instruction fine-tuning to improve model relevance and performance. Preparation suggestion: explain how you would define the target tasks, assess training examples, and compare the resulting model with a baseline.
How would you approach adapting an LLM to a new domain?
What to coverDomain adaptation is an explicit part of the role. Preparation suggestion: discuss how you would identify domain-specific gaps, choose suitable data, and check whether improvements generalize beyond a narrow test set.
When would you use prompt engineering rather than fine-tuning?
What to coverThe posting names both prompt engineering and fine-tuning. Preparation suggestion: compare them in terms of the change needed, available data, iteration effort, and how you would evaluate the choice.
How would you improve a prompt that produces inconsistent outputs?
What to coverStrong prompt-engineering knowledge is requested. Preparation suggestion: describe a controlled way to revise instructions and examples, test representative inputs, and determine whether the inconsistency persists.
How would you translate a business challenge into an enterprise GenAI solution design?
What to coverThe role leads solution design and delivery for business challenges. Preparation suggestion: explain how you would define the task, fit the solution into an existing workflow, and identify a measurable outcome before selecting an approach.
When would an agentic AI approach be appropriate for process automation?
What to coverThe posting calls for Agentic AI solutions that automate processes. Preparation suggestion: contrast a multi-step agentic approach with a simpler workflow, including how you would keep actions within the intended task.
How would you investigate failures in an agentic workflow?
What to coverDelivering Agentic AI for enterprise workflows makes troubleshooting a relevant practice topic. Preparation suggestion: trace where a task went wrong, distinguish model-output problems from workflow-design problems, and explain how you would verify a fix.
How would you evaluate whether an LLM solution is ready for its intended use?
What to coverThe posting requests knowledge of AI evaluation frameworks and measurable impact. Preparation suggestion: define task-relevant quality measures, use representative cases, and explain how evaluation results would inform a delivery decision.
What would you consider when scaling and deploying an enterprise GenAI solution?
What to coverDesign, deployment, and scalability are stated responsibilities or considerations. Preparation suggestion: discuss how you would assess performance under expected use and weigh operational complexity against the business value delivered.
How would you account for responsible use in an LLM solution?
What to coverResponsible use is explicitly named alongside scalability and business value. Preparation suggestion: describe risks relevant to the intended workflow, ways to evaluate them, and trade-offs you would raise before deployment.
Behavioural practice
Tell me about a time you led an AI solution from design toward delivery.
What to coverThe role calls for leading AI solution design and delivery. Use a real situation, the decisions and actions you owned, and the resulting outcome; distinguish your contribution from the wider team's.
Describe a time you aligned a technical approach with a business objective.
What to coverAligning AI capabilities with business objectives and tangible value is explicit in the posting. Give a real example showing the objective, how it shaped your actions, and what evidence indicated the result.
Tell me about a time you integrated an AI capability into an existing workflow.
What to coverThe role includes process automation and integrating intelligent insights into enterprise workflows. Describe the original workflow, what you changed, and the observed effect without assuming the change was successful.
Describe a time you had to balance delivery goals with responsible AI use.
What to coverThe posting asks for responsible use as well as measurable value. Use a real situation to explain the competing considerations, your actions, and the outcome or remaining limitation.