Technical practice
How would you move an AI proof of concept toward production readiness?
What to coverSuggested answer focus: Explain how you would assess reliability, scalability, maintainability and alignment with the business objective before expanding use. Describe the trade-off between improving a promising prototype and narrowing its scope to deliver a dependable feature.
How would you turn an AI feature request into a user story and acceptance criteria?
What to coverSuggested answer focus: Clarify the user's need and the business outcome, then define observable criteria for success and failure. Explain how you would resolve an ambiguous request before implementation.
What makes Python code for an AI feature production-ready?
What to coverSuggested answer focus: Discuss clear interfaces, error handling and maintainable structure, alongside a Git workflow that makes changes reviewable. Explain what you would examine when assisting with a code review.
How would you design unit and smoke tests for an AI feature?
What to coverSuggested answer focus: Distinguish unit tests for isolated behavior from smoke tests that check whether a deployed feature's essential path works. Address how to test predictable components while handling variability in AI outputs.
How would you triage an AI feature that starts producing incorrect results?
What to coverSuggested answer focus: Describe how you would reproduce the issue, assess its impact, isolate the failing component and check recent changes. Explain how iterative testing would confirm a fix without introducing a regression.
What would you examine in an ethical, risk and security review of an AI feature?
What to coverSuggested answer focus: Consider inappropriate outputs, sensitive information, access controls and potential harm in the feature's intended use. Explain how you would document a risk and weigh mitigation against deployment readiness.
How would you evaluate changes to a prompt?
What to coverSuggested answer focus: Compare outputs against representative requests and the feature's acceptance criteria rather than relying on a few favorable examples. Discuss trade-offs such as consistency, usefulness and the effort needed to maintain the prompt; prompt engineering is a preferred skill in the posting.
How would you assess whether retrieval-augmented generation fits an AI feature?
What to coverSuggested answer focus: Explain what retrieving source material could add and how you would evaluate whether the retrieved material supports the answer. Discuss failure cases such as irrelevant retrieval or an answer that goes beyond its sources; RAG knowledge is preferred, not listed as required.
When might you consider LLM fine-tuning instead of prompt changes or RAG?
What to coverSuggested answer focus: Compare the approaches in terms of the behavior needed, available examples, evaluation effort and maintenance. Frame this as a decision method rather than assuming the role uses a fine-tuned model; fine-tuning experience is preferred.
How would you evaluate an AI tool for a business feature?
What to coverSuggested answer focus: Define the feature's requirements, then compare candidate tools on suitability, integration effort, risk and ability to meet acceptance criteria. ChatGPT Enterprise, Microsoft Copilot and Azure AI Foundry appear as preferred experience in the posting; do not assume any one is used for every solution.
Behavioural practice
Tell me about a time you made progress on an ambiguous technical task while working independently.
What to coverSuggested answer focus: Use a real situation, your actions to clarify priorities and test assumptions, and the result. Connect the example to the role's stated need for independent work and iterative refinement without claiming a particular Crowe process.
Describe a time you explained a technical decision to a nontechnical stakeholder.
What to coverSuggested answer focus: Give the situation, how you documented or presented the options in terms of the stakeholder's business need, and the result. Make clear how you checked that the decision and its limitations were understood.
Tell me about a time a code review improved a solution.
What to coverSuggested answer focus: Describe the situation, the feedback you gave or received, what changed and the result. Emphasize how the interaction improved code quality while keeping the work collaborative.
Describe a time you raised an ethical, risk or security concern about a technical solution.
What to coverSuggested answer focus: State the situation, the concern, how you communicated or investigated it, and the result. Show how you balanced delivery with responsible deployment and integrity, both of which are reflected in the posting.