Technical practice
How would you decide whether and how to fine-tune a foundation model for this platform?
What to coverThe role explicitly includes fine-tuning foundation models. Suggested preparation: explain how you would define the desired improvement, establish a baseline, and weigh quality gains against the cost and complexity of tuning.
How would you prepare and check data for foundation-model fine-tuning?
What to coverSuggested preparation: discuss data relevance, quality checks, and separation of training and evaluation examples. Data preparation is an inferred topic relevant to the stated fine-tuning responsibility, not a specified requirement.
How would you evaluate whether fine-tuning improved the generative AI solution?
What to coverSuggested preparation: compare outputs against a baseline using criteria tied to the intended use, and examine failures as well as aggregate results. Explain how you would check that an improvement on evaluation examples remains useful to customers.
How would you design and test prompts for the platform?
What to coverPrompt engineering is an explicit responsibility. Suggested preparation: describe how you would specify the task and desired output, test representative inputs, and revise prompts when results are inconsistent or unhelpful.
When would you change a prompt rather than fine-tune a model?
What to coverBoth approaches appear in the posting. Suggested preparation: compare their suitability for a recurring problem in terms of observed quality, iteration effort, and ongoing maintenance, rather than assuming one is always better.
How would you identify an algorithmic optimization worth making?
What to coverThe role includes optimizing algorithms to enhance the solution. Suggested preparation: start with a measurable problem, identify its likely cause, and explain how you would verify that a change improves the result without unacceptable quality trade-offs.
How would you assess whether a generative AI feature can scale on AWS?
What to coverThe posting places the SaaS solution on AWS and calls out scalability, but names no specific services. Suggested preparation: discuss measuring workload, response time, and resource use as demand grows, then explain the trade-offs you would investigate.
How would you tell whether a model or prompt change helps customers use the solution effectively?
What to coverCustomer usefulness is a stated goal of the role. Suggested preparation: connect a proposed change to a defined customer task, assess the usefulness of its outputs, and consider whether improvements hold across varied inputs.
Behavioural practice
Tell me about a time you improved an AI result that initially fell short.
What to coverSuggested preparation: use a real example to describe the situation, the evidence you gathered, and the action you took. End with the observed result and what it did or did not establish about the improvement.
Describe a time you balanced solution quality against performance or scale.
What to coverSuggested preparation: choose a real situation with competing goals, explain the options you considered and your action, then state the measured result. Relate the trade-off to the posting's emphasis on optimization and scalability.
Tell me about a time you worked with others to resolve an unclear technical goal.
What to coverThe posting says the engineer will join a team but does not specify how it works. Suggested preparation: describe the situation, how you clarified the goal with others, your contribution, and the resulting decision or outcome.
Describe a time you changed a technical approach after learning it was not useful to its intended users.
What to coverSuggested preparation: give a real situation, explain what indicated the gap between the output and user needs, and describe your action and its result. This relates to the posting's goal of helping customers use AI effectively; direct customer contact is not stated.