Lead AI Technical Product Manager

What matters most

• Select AI opportunities with a defensible business case

  Map business operations with business units, scope ambiguous problems into AI use cases, and set a vision and roadmap. The posting calls for value propositions and ROI justifications that guide prioritization as business needs and models change.

• Prove feasibility before scaling delivery

  Personally build working prototypes and demos with agentic coding and AI tools before committing a full team. Turn validated use cases into requirements, edge cases, acceptance criteria, success metrics and a usable end-to-end experience.

• Own the path from prototype to production

  Manage the backlog, sequence work, make scope trade-offs and drive launch and iteration. Support architecture discussions with engineering while owning relevant prompt-engineering or AI-harness skill components.

• Make evaluation drive product decisions

  Define quality with golden sets, ground truth, offline and production evaluations, and appropriate measures such as precision, recall, task success and hallucination rate. Use validated LLM-as-judge approaches where appropriate and explain results to leadership.

• Lead repeatable, governed AI delivery

  Guide solutions through Humana’s Enterprise AI Governance Framework, including stage-gate reviews, documentation and scorecards. Lead cross-functional partners without direct authority, develop reusable patterns and playbooks, and mentor product managers.

Likely challenges to think through

• Balancing prototype speed with production readiness

  Preparation hypothesis: a convincing demo may not yet establish usability, feasibility at scale or measurable value. Prepare to explain how you would decide whether to commit a team, then translate a validated prototype into acceptance criteria and a sequenced backlog.

• Choosing quality thresholds amid competing failure costs

  Preparation hypothesis: improving recall may increase false positives, while a strong offline result may not hold in production. Prepare an example of selecting ground truth, metrics and live checks for a use case, and of using the evidence to make a launch or iteration decision.

• Maintaining momentum through governance reviews

  Preparation hypothesis: rapid AI iteration and required stage gates may pull schedules in different directions. Prepare to describe how documentation, scorecards and review needs would be planned alongside delivery rather than treated as an afterthought.

• Aligning teams as models and priorities shift

  Preparation hypothesis: changing model capabilities or business needs could unsettle a roadmap owned across teams without direct authority. Prepare to explain how you would make trade-offs visible, communicate ROI and evaluation results to technical and executive audiences, and preserve reusable solution patterns.

Topics to prepare

• AI product strategy and investment decisions

  The role owns vision, roadmap, prioritization, business cases and ROI. Revise how to compare competing use cases: sketch a decision framework that weighs business value, feasibility, adoption and changing model capabilities.

• LLMs, agents and RAG

  Working knowledge of these approaches and prompt engineering is required. For one hypothetical enterprise task, explain when retrieval, an agent or a simpler prompt would be appropriate, and what feasibility questions you would take to engineering. GraphRAG and knowledge graphs are preferred, not required.

• AI evaluation and launch decisions

  The posting calls for golden sets, ground truth, offline and production evaluation, precision- and recall-based metrics, task success and hallucination rate. Revise which measures answer different product questions; explain when LLM-as-judge would need validation before use.

• Governed AI delivery

  All AI solutions must pass Humana’s Enterprise AI Governance Framework stage gates; the role owns documentation, scorecards and reviews involving AIRB, LRC and the Responsible AI Council. Practice outlining what product evidence you would bring to a review without assuming the posting specifies each body’s criteria.

• Production delivery and systems thinking

  The role spans prototype, launch and iteration, including scope tradeoffs and reusable solution patterns. Revisit a hypothetical recurring AI failure and outline both an immediate backlog decision and a reusable change that could prevent the broader class of failures.

Skills to revise

• Map an operation and model its value

  Practice drawing a simple end-to-end business workflow, identifying one bounded AI use case, and calculating illustrative costs, benefits and ROI assumptions. This mirrors the role’s service-design discovery and investment justification; the workflow is a preparation example, not an assigned Humana project.

• Build and critique a working demo

  Using one example tool named in the posting—Claude Code, Cursor or OpenAI Codex—prototype a small AI-assisted workflow with sample data. Record a specific model-behavior defect, the prompt or harness change you would try, and how you would test whether it helped. Hands-on prototyping is explicitly required.

• Construct an evaluation set and readout

  Create a small labeled golden set with edge cases, run an offline comparison, calculate precision, recall and F1 where appropriate, and draft a plan for production evaluation. Summarize what the results would and would not justify at launch; building and using evaluations is central to the posting.

• Turn a validated use case into a backlog

  Write a few user stories with acceptance criteria, failure cases and success metrics, then sequence them from demo to production. Explain one scope tradeoff you would make if model behavior changed. The role owns requirements, backlog and delivery decisions.

• Present a cross-functional product decision

  Prepare two brief versions of one hypothetical roadmap and evaluation decision: one for engineers and data scientists, another for executives. Include the ROI assumptions, quality evidence, open risks and next decision. The posting requires leadership without direct authority and communication across technical and executive audiences.

• Map the required evidence

  Prepare a brief inventory of relevant examples against the posted requirements: 7+ years of product or related experience, including 4+ in product management; AI/ML or GenAI products shipped to production; hands-on prototyping; and AI evaluation. Note the degree or equivalent-experience requirement without assuming a match.

• Build a product discovery walkthrough

  Prepare one example showing how an ambiguous enterprise problem became an end-to-end operations map, scoped AI use case, value proposition, ROI model, and prioritized roadmap. Bring the assumptions and tradeoffs behind the investment decision.

• Prepare a hands-on prototype demonstration

  If you have a suitable example, show what you built yourself with an AI or agentic coding tool, what the demo tested, and what you would change about the model’s behavior and why. The posting names Claude Code, Cursor, and OpenAI Codex as examples, not mandatory tools.

• Draft a buildable design exercise

  For a hypothetical enterprise AI use case, prepare a one-page sketch of the user journey, architecture and feasibility questions, prompt or harness components, user stories, acceptance criteria, edge cases, and success metrics. Label the scenario as hypothetical.

• Show how evaluation drives decisions

  Prepare an evaluation plan or past example covering golden sets, ground truth, offline and production checks, and appropriate precision-, recall-, task-success-, or hallucination-based measures. Explain when an LLM-as-judge would need validation and how results would inform a launch decision.

• Trace delivery through governance

  Prepare a concise account of backlog sequencing, scope changes, cross-functional decisions, and iteration after launch. Separately outline the product documentation, scorecards, and stage-gate questions you would expect to discuss; the posting names AIRB, LRC, and the Responsible AI Council but does not specify their review criteria.

• Prepare leadership examples and employer questions

  Select examples of influencing engineers, data scientists, designers, and business partners without direct authority, plus any reusable playbook or mentoring work. Ask how this portfolio sets priorities, validates evaluations, and coordinates governance reviews; confirm expectations for the three-office-day hybrid schedule if relevant.