Technical practice
Suggested practice: How would you prioritize an AI product roadmap when opportunities and model capabilities are changing?
What to coverThe role owns vision, strategy, roadmap and priorities for a complex AI product area. For practice, explain how you would compare expected value, feasibility and evidence of user need, then revisit priorities as conditions change.
Suggested practice: How would you turn an ambiguous business operation into an AI use case and ROI case?
What to coverThe posting calls for mapping operations end to end and developing scoped use cases, value propositions and ROI justifications. For practice, describe what you would learn from business partners, how you would define a baseline, and which assumptions you would test before seeking investment.
Suggested practice: What would you build into a hands-on AI prototype before committing a full team?
What to coverThe role involves personally building working demos with agentic coding and AI tools to test feasibility and value. For practice, distinguish what a prototype must demonstrate from what production delivery must establish, and explain how its results would inform a decision.
Suggested practice: How would you specify a validated AI use case for engineering, data science and design?
What to coverThe posting names requirements, user stories, acceptance criteria, edge cases, success metrics and end-to-end usability. For practice, explain how you would make expected behavior and failure cases testable while leaving room for design and technical trade-offs.
Suggested practice: How would you take an AI product from prototype through launch and iteration?
What to coverThe role owns the backlog, work sequencing and scope decisions as models and constraints shift. For practice, explain how you would resolve a delivery trade-off, determine what is ready to launch, and use post-launch evidence to reprioritize.
Suggested practice: How would you construct a golden set and choose evaluation metrics?
What to coverThe posting calls for ground truth, golden sets, offline and production evaluations, and measures including precision, recall, F1, task success and hallucination rate. For practice, explain how you would establish trustworthy reference answers and choose metrics according to the consequences of different errors.
Suggested practice: What would you do if offline evaluations looked strong but production quality deteriorated?
What to coverThe role uses both offline and live evaluations to inform launch decisions and leadership readouts. For practice, describe how you would investigate gaps between the test set and live tasks, check whether an LLM-as-judge measure had been validated, and communicate a decision based on the evidence.
Suggested practice: When would you consider an LLM, agent, RAG or graph-based approach?
What to coverThe posting requires working knowledge of LLMs, agents and RAG, and mentions graph and GraphRAG approaches. For practice, compare them against the task's information and workflow needs, explaining what you would test with engineering before choosing added complexity.
Suggested practice: How would you improve a model's behavior through prompts or reusable skills?
What to coverThe role owns relevant prompt-engineering or skill-creation components of the AI harness and asks for specific reasoning about model behavior. For practice, identify an observable failure, propose a targeted change, and explain how you would evaluate whether it helped without creating new regressions.
Suggested practice: How would you prepare an AI product for governed stage-gate review?
What to coverThe posting says all AI solutions must follow Humana's Enterprise AI Governance Framework and names product documentation, scorecards and stage-gate reviews. For practice, explain how you would connect the use case, quality evidence, limitations and launch decision in a clear review package without assuming unstated review criteria.
Behavioural practice
Suggested practice: Tell me about a time you aligned a cross-functional team without direct authority.
What to coverThe role leads engineers, data scientists, designers and business partners without direct authority. Use a real situation, the actions you took to resolve differing priorities, and the resulting decision or delivery outcome.
Suggested practice: Tell me about a time you worked from first principles through an ambiguous product problem.
What to coverBusiness discovery and comfort with ambiguity are prominent in the posting. Give the situation, how you clarified the problem and tested assumptions, and what the result taught you about the use case.
Suggested practice: Tell me about a time you prevented a recurring class of product or delivery problems.
What to coverThe posting emphasizes systems thinking and reusable delivery patterns rather than one-off fixes. Describe the recurring issue, the infrastructure or pattern you introduced, and evidence of its effect.
Suggested practice: Tell me about a time you communicated an AI product trade-off to technical and executive audiences.
What to coverThe role communicates roadmap, ROI and evaluation results across both audiences. Set out the situation, how you adapted the evidence and recommendation for each audience, and the decision or outcome.
Suggested practice: Tell me about a time you mentored product managers while maintaining delivery momentum.
What to coverThe posting calls for mentoring product managers and raising the team's standard for building with AI. Use a real example showing the situation, the guidance you provided, and a concrete result for the colleague, team or product work.