Principal Product Manager, AI

What matters most

• Shape the AI portfolio

  The role defines a multi-year vision and roadmap, maps operations with business leaders, and sequences ambiguous use cases by business case. Prepare to explain how you would compare opportunities and communicate investment choices to executives.

• Prove ideas hands-on

  Humana expects working prototypes and demos using agentic coding tools before committing a full team. Validated ideas must become user stories, acceptance criteria, edge cases, success metrics, and usable experiences.

• Deliver reusable production patterns

  The role drives products from prototype through launch and iteration, partners with engineering on feasibility, and owns reusable context-engineering and AI-harness components rather than only one-off solutions.

• Set evaluation standards

  The posting calls for golden sets, error budgets, offline and production evaluations, and metrics such as precision, recall, task success, and hallucination rate. Results inform launch decisions and leadership readouts.

• Lead governed adoption

  The role owns documentation, scorecards, and stage-gate reviews for AI governance, with safety, fairness, privacy, and compliance built in. It also leads across functions without direct authority, mentors product managers, and develops playbooks.

Likely challenges to think through

• Keep a long-term roadmap adaptable

  Likely trade-off: multi-year commitments must coexist with changing business needs and model capabilities. Prepare a prioritization approach that states assumptions, compares business cases, and identifies when evidence would trigger resequencing.

• Turn a compelling demo into a viable launch

  Likely difficulty: a fast prototype may not establish usability, feasible architecture, or acceptable latency, cost, and hallucination risk at production scale. Prepare to describe decision gates from prototype to scoped requirements, engineering validation, launch, and iteration.

• Balance launch pace with evidence and governance

  Likely trade-off: teams need momentum while evaluations and responsible-AI stage gates require defensible evidence. Prepare an example framework linking ground truth, error budgets, production monitoring, and safety or privacy findings to a launch decision.

• Standardize without stalling teams

  Likely difficulty: shared harness patterns, evaluation standards, and playbooks must work across distinct products and stakeholders the role does not directly manage. Prepare to explain how you would establish a reusable baseline, allow justified exceptions, and gain adoption through clear technical and business reasoning.

Topics to prepare

• Portfolio strategy under uncertainty

  The posting calls for a multi-year AI portfolio vision, first-principles prioritization and a roadmap that adapts as business needs and models change. To revise, compare three hypothetical use cases by value, feasibility and risk, then explain how a model improvement would change their sequence.

• LLM solution trade-offs

  Required knowledge includes LLMs, agents, RAG, prompt and harness engineering, plus context-window, latency, cost, hallucination and RAG-versus-fine-tuning trade-offs. To revise, make a decision table for a hypothetical enterprise task and defend the simplest viable approach.

• Evaluation and launch decisions

  The role sets standards for golden sets, error budgets, offline and production evaluations, and metrics such as precision, recall, task success and hallucination rate. To revise, choose metrics for two different failure modes and state what result would block launch; include when an LLM-as-judge would need validation.

• Reusable AI architecture

  The posting emphasizes systems thinking, engineering partnership, and reusable context-engineering, skill-creation and AI-harness patterns. To revise, sketch a high-level pattern shared by two hypothetical products and identify which components should remain product-specific.

• Responsible AI and governed delivery

  Humana says shipped solutions must clear its AI governance stage gates, with safety, fairness, privacy and compliance built in. To revise, map those four concerns to evidence you would seek before a stage-gate review; do not assume the contents of Humana's internal standards.

• Influence at principal level

  The role leads cross-functional teams without direct authority, influences VP-level organizations and mentors senior and lead product managers. To revise, outline how you would resolve a hypothetical disagreement between business value, usability, technical feasibility and evaluation results.

Skills to revise

• Build a sequenced business case

  The posting asks for end-to-end operational discovery, AI use-case selection, ROI models and executive roadmaps. As practice, draft a one-page, explicitly hypothetical portfolio proposal with assumptions, expected value, dependencies and reasons for sequencing.

• Prototype and explain technical choices

  Working demos using agentic coding tools such as Claude Code, Cursor or OpenAI Codex are explicitly part of the role. As practice, use one named tool to prototype a small non-sensitive task, then record what you would change about the model's behavior and why.

• Construct and interpret an evaluation

  The posting requires hands-on golden sets, ground truth, offline and live evaluations, and standards other teams can adopt. As practice, create a small labeled test set, score sample outputs for precision and recall, inspect failures, and write a launch recommendation tied to an error budget.

• Turn validation into buildable scope

  The role translates validated use cases into user stories, acceptance criteria, edge cases and success metrics while partnering with design and engineering. As practice, write those artifacts for one hypothetical prototype and identify a usability and an architecture question for the respective partners.

• Present a governed launch decision

  The posting calls for product documentation, scorecards, stage-gate reviews and clear executive communication of ROI and evaluation results. As practice, prepare a brief hypothetical leadership readout covering value, test results, safety and privacy concerns, unresolved risks, and a launch-or-iterate recommendation.

• Draft a portfolio strategy brief

  Using an illustrative enterprise problem, map the workflow, propose several AI use cases, and sequence them on a multi-year roadmap. State assumptions, business-case estimates, and what would change your priorities.

• Rehearse a production-product walkthrough

  If you have a relevant AI/ML launch, prepare a concise account of your role from discovery and prototype through production and iteration, including scope trade-offs, measured outcomes, and executive decisions.

• Prepare a hands-on prototype demonstration

  Show a prototype you built, or outline one you could build, using non-sensitive data. Explain its feasibility test and your choices around model behavior, context, agents, RAG versus fine-tuning, latency, cost, and hallucination risk.

• Write a buildable design exercise

  For the illustrative use case, produce user stories, acceptance criteria, edge cases, success metrics, and a simple user flow. Note what you would validate with design and engineering before committing to delivery.

• Create an evaluation and launch scorecard

  Specify a golden set and ground truth, an error budget, offline and production evaluations, and relevant precision, recall, task-success, and hallucination measures. Show how results would inform a launch decision; use LLM-as-judge only with a validation plan.

• Map a governed delivery path

  Draft a sample product-documentation and review checklist covering safety, fairness, privacy, and compliance. The posting names AIRB, LRC, and the Responsible AI Council; prepare to ask how their stage gates work rather than assuming their criteria.

• Collect evidence of influence and reuse

  If available, select examples of leading across disciplines without direct authority, communicating ROI or evaluation results to executives, mentoring product managers, and establishing reusable AI patterns or playbooks. Identify the decision and outcome for each.

• Prepare questions for Humana

  Ask how portfolio priorities are set, who owns evaluation thresholds and launch decisions, how governance reviews affect delivery, and which context-engineering or harness patterns the team hopes to reuse.