New opportunity

Lead AI Technical Product Manager

Humana · New York, United States of America

About this role

Humana seeks a hands-on Lead AI Technical Product Manager to lead a complex area of its AI portfolio, from identifying enterprise problems and prototyping solutions through production delivery and AI governance. The role works across engineering, design, data science and business teams.

Responsibilities

Define the product vision, strategy and roadmap; map business operations and turn ambiguous problems into scoped AI use cases supported by value propositions, business cases and ROI estimates.
Build prototypes and demos using agentic coding and AI tools such as Claude Code, Cursor and OpenAI Codex. Translate validated ideas into requirements, user stories, acceptance criteria, edge cases and success metrics, and shape the user experience with designers.
Own the backlog and guide delivery from prototype through launch and iteration, making scope and prioritization decisions as models and constraints change. Support architecture discussions and own required prompt-engineering or skill-creation components of the AI harness.
Define quality using golden sets, ground truth, offline and live evaluations, and appropriate metrics such as accuracy, precision, recall, F1, task success and hallucination rate. Use LLM-as-judge where validated, and communicate evaluation findings and launch decisions to leadership.
Lead cross-functional partners without direct authority, create reusable delivery patterns, mentor product managers and guide solutions through Humana’s Enterprise AI Governance Framework, including documentation, scorecards and stage-gate reviews.

Required qualifications

A B.S. or M.S. in Computer Science, Engineering or a related field, or equivalent experience; 7+ years of product or related experience, including 4+ years in product management; and experience shipping AI/ML or GenAI products to production. Candidates must have hands-on AI prototyping and evaluation experience, systems thinking, strong business and technical communication, and working knowledge of LLMs, agents, RAG and prompt engineering.

Preferred qualifications

Experience in healthcare, insurance or another regulated industry; hands-on experience with agent frameworks, GraphRAG, knowledge graphs or reusable skills and plugins; and comfort working from first principles in a fast-paced, ambiguous setting.

The full-time role is scheduled for 40 hours weekly. It requires three office days per week, with remaining days remote. Candidates must live within, or be willing to relocate within, commuting distance of Louisville, Chicago, New York, Dallas, Boston or Washington, DC. Estimated starting base pay is $151,600–$208,400 annually, with eligibility for a performance-based bonus incentive plan. Benefits include medical, dental and vision coverage, a 401(k), paid time off and leave, disability coverage and life insurance.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

AI product managementProduct strategyRoadmappingBusiness discoveryService designBusiness case developmentROI modelingAI prototypingClaude CodeCursorOpenAI CodexRequirements gatheringUser storiesAcceptance criteriaBacklog managementAI evaluationGolden setsGround truthOffline evaluationProduction evaluationPrecisionRecallF1 scoreLLM-as-judgePrompt engineeringSolution architectureCross-functional leadershipAI governanceResponsible AILarge language models (LLMs)

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Choose AI opportunities with a defensible business case
Own the vision and roadmap for a complex AI product area. Map business operations end to end, scope ambiguous problems into AI use cases, and justify investment with value propositions and ROI estimates.
Prove ideas hands-on before scaling them
Build working prototypes and demos yourself using agentic coding and AI tools before committing a full team. Turn validated ideas into requirements, acceptance criteria, edge cases and success metrics, while shaping the user experience with design.
Carry products from prototype to production
Own the backlog and delivery sequence, make scope decisions as models and constraints change, and support engineering’s architecture discussions. Own required prompt-engineering or skill-creation components of the AI harness.
Make evaluation drive launch decisions
Define quality with golden sets, ground truth, offline and live evaluations, and metrics suited to the use case. Use LLM-as-judge only where validated, then explain findings and launch decisions to leadership.
Lead governed, reusable AI delivery
Align engineering, data science, design and business partners without direct authority. Own product documentation, scorecards and stage-gate reviews under Humana’s Enterprise AI Governance Framework; develop reusable patterns and mentor product managers.

Likely challenges to think through

Balance opportunity value against uncertain feasibility
A plausible challenge is deciding when an attractive use case merits a full team, given ambiguous operations and changing model capabilities. Prepare a decision framework that links workflow mapping and ROI assumptions to a small prototype, explicit success criteria and a stop-or-proceed decision.
Set quality thresholds without hiding consequential errors
A likely difficulty is trading precision against recall while ground truth, hallucination rates and live behavior may tell different stories. Prepare to explain how you would construct representative golden sets, validate any LLM-as-judge method, and use offline and production results to support a launch decision.
Preserve delivery pace through governance and shared ownership
The role combines shifting backlog decisions, leadership without direct authority and mandatory governance stage gates. Prepare an approach for aligning partners on scope and evidence early, maintaining documentation and scorecards as work evolves, and making trade-offs visible without assuming reviews can be bypassed.

Practice your answers

Technical practice

How would you turn an ambiguous business problem into a scoped AI use case?

What to coverSuggested answer focus: Map the operation end to end with business partners, identify the user need and define a testable value proposition. Explain how you would estimate benefits, costs and uncertainty before recommending investment.

How would you prioritize a roadmap for a complex AI product area?

What to coverSuggested answer focus: Compare opportunities by business value, feasibility, evaluation evidence and delivery constraints. Explain what would cause you to revise priorities as models or business needs change.

What would you build to test an AI idea before committing a full team?

What to coverSuggested answer focus: Describe a small working prototype or demo, what an agentic coding tool could help you test, and the evidence needed to proceed. Distinguish a convincing demonstration from proof of production value.

How would you turn a validated prototype into buildable requirements?

What to coverSuggested answer focus: Cover user stories, acceptance criteria, edge cases and measurable success criteria. Explain how you would work with design on the end-to-end experience so usability is tested alongside model behavior.

How would you assess whether an enterprise use case needs an LLM, RAG or an agent?

What to coverSuggested answer focus: Compare the work each approach must perform, the information it needs and the complexity it adds. If discussing GraphRAG or knowledge graphs, identify them as approaches named in the posting’s preferred qualifications and explain what evidence would justify using them.

How would you diagnose and improve a model behavior that fails an AI workflow?

What to coverSuggested answer focus: Isolate the failure with representative examples, then explain what you would change in prompts or reusable harness skills and why. Re-evaluate the change against the original failure and other important cases rather than relying on a single improved output.

How would you build a golden set and ground truth for an AI product?

What to coverSuggested answer focus: Define representative tasks, edge cases and an approach to establishing reliable expected outcomes. Explain how you would keep the set useful as the workflow changes and separate development examples from checks used to assess quality.

How would you use offline and live evaluations to make a launch decision?

What to coverSuggested answer focus: Match accuracy, precision, recall, F1, task success or hallucination rate to the product risk and the cost of different errors. Explain how you would validate any LLM-as-judge approach, compare offline findings with production results and present a clear launch recommendation.

How would you manage the backlog from prototype through launch and iteration?

What to coverSuggested answer focus: Explain how you would sequence feasibility work, product requirements and quality checks while keeping engineering, data science and design aligned. Describe how changing model capabilities or constraints would affect scope and trade-off decisions.

How would you prepare an AI product for Humana’s governance stage gates?

What to coverSuggested answer focus: The posting names the Enterprise AI Governance Framework, AIRB, LRC, Responsible AI Council, documentation and scorecards. Explain how you would organize product evidence and evaluation results for review without assuming unstated approval criteria.

Behavioural practice

Tell me about a time you brought structure to an ambiguous product problem.

What to coverSuggested answer focus: Use situation, action and result to show how you mapped the problem, tested assumptions and defined a tractable first step. Be specific about what changed in the product decision.

Tell me about a time you aligned cross-functional partners without direct authority.

What to coverSuggested answer focus: Use situation, action and result to describe differing engineering, design, data science or business perspectives. Explain how you resolved a concrete disagreement and what the team delivered or decided.

Tell me about a recurring delivery or quality problem you addressed at the system level.

What to coverSuggested answer focus: Use situation, action and result to distinguish the immediate fix from a reusable playbook, skill or solution pattern. Explain how you determined whether the broader change prevented similar failures.

Tell me about a time you communicated difficult evaluation findings to decision-makers.

What to coverSuggested answer focus: Use situation, action and result to explain the evidence, uncertainty and trade-offs you presented to technical and executive audiences. State how the findings affected the decision, without treating a launch as the only successful outcome.

Tell me about a time you helped another product manager improve their approach to AI delivery.

What to coverSuggested answer focus: Use situation, action and result to describe the guidance you gave on scoping, prototyping or evaluation. Identify an observable change in their practice or the team’s work rather than a general mentoring claim.

Sharpen your knowledge

Topics to prepare

AI product strategy and service design
The role owns a complex product area and turns end-to-end business operations into prioritized AI use cases. Revision exercise: sketch an illustrative workflow, identify two competing opportunities, and explain which you would prioritize using value, feasibility and ROI assumptions.
LLMs, agents, RAG and prompt engineering
Working knowledge of these approaches is required. Revision exercise: compare a prompt-only, RAG and agent-based approach to one illustrative enterprise task; explain what each can and cannot reliably do, and what evidence would justify the added complexity.
Evaluation methodology and launch decisions
The posting calls for golden sets, ground truth, offline and production evaluations, and precision- and recall-based decisions. Revision exercise: choose metrics for an illustrative task, explain the cost of false positives versus false negatives, and state when an LLM-as-judge result would need validation.
Prototype-to-production tradeoffs
This lead owns the backlog and adapts scope as models and constraints change while supporting architecture discussions. Revision exercise: take an illustrative prototype with inconsistent outputs and reason through whether to change its prompt, narrow its scope, revise its design or delay launch.
Enterprise AI governance and reusable patterns
Humana states that AI solutions undergo full review at designated stage gates under its Enterprise AI Governance Framework; the role owns product documentation and scorecards for AIRB, LRC and Responsible AI Council reviews. Revision exercise: outline what evidence from a hypothetical product you would bring to a review, without assuming unstated approval criteria, and identify one reusable pattern that could prevent a recurring issue.

Skills to revise

Build and critique a working AI demo
The role explicitly builds its own prototypes using agentic coding and AI tools; Claude Code, Cursor and OpenAI Codex are examples in the posting. Practice making a small demo for an illustrative enterprise task, then record one observed model-behavior failure, the change you made and whether it improved the result.
Create and use an evaluation set
Hands-on evaluation is required. Practice labeling a small golden set with ground truth, calculating precision, recall and F1 where appropriate, and drafting an offline-versus-live evaluation plan. Explain how the results would affect a launch decision.
Scope a use case and estimate value
The role maps operations and prepares value propositions, business cases and ROI justifications. Practice turning an ambiguous illustrative workflow into a bounded use case; show the assumptions behind a simple benefits-and-costs estimate rather than presenting a speculative figure as fact.
Write buildable requirements and sequence a backlog
The posting specifies user stories, acceptance criteria, edge cases, success metrics and backlog ownership. Practice writing one story with testable criteria and failure cases, then order several follow-up items and explain a scope tradeoff after an evaluation result changes.
Present a governed launch recommendation
The role communicates evaluation results to leaders and owns governance documentation, scorecards and stage-gate reviews. Practice a short technical-and-executive readout that states the use case, measured quality, remaining risks, recommendation and evidence still needed for review; do not presume Humana's unstated thresholds.
Facilitate cross-functional decisions
The role leads engineering, data science, design and business partners without direct authority and mentors product managers. Practice chairing an illustrative decision on a usability-versus-quality tradeoff: elicit each discipline's concern, document the decision and owner, and turn the lesson into a brief reusable playbook entry.

Your preparation checklist

Select a production AI product walkthrough
Prepare a concise account of an AI/ML or GenAI product you helped ship, if available: your decisions, prototype-to-launch timeline, outcomes, and evidence of product ownership. The posting requires production delivery and senior product experience.
Draft a business discovery case
As an interview exercise, map a hypothetical enterprise workflow, identify one bounded AI use case, and write its value proposition, assumptions, ROI estimate, and reasons to prioritize it. Humana lists service design, business cases, and roadmap decisions as role responsibilities.
Prepare a hands-on prototype demonstration
Bring a small, safe-to-share demo or walkthrough showing what you built with an AI coding tool, what feasibility question it tested, and which model behavior you would change and why. The role calls for personally building prototypes before committing a full team.
Turn the use case into a delivery plan
Create a one-page user flow, sample user stories, acceptance criteria, edge cases, success metrics, and prioritized backlog. Be ready to explain architecture tradeoffs with engineering and how scope could change between prototype and launch.
Build an evaluation and launch-decision example
Prepare a sample golden set and ground-truth approach, offline and live evaluation plan, and a brief leadership readout. Explain when precision, recall, task success, or hallucination rate matters, and use LLM-as-judge only where validated.
Rehearse a cross-functional leadership example
Outline a real example, if available, of resolving competing priorities across business, design, engineering, and data science without direct authority. Identify the decision made and any reusable pattern or mentoring contribution rather than claiming sole credit.
Sketch a governance review packet
Draft an illustrative product summary, evaluation scorecard, risks, and open decisions for a stage-gate review. The posting names Humana’s Enterprise AI Governance Framework, AIRB, LRC, and Responsible AI Council; do not assume their specific review criteria.
Write targeted questions for Humana
Ask how this product area is selected, what evidence supports a launch decision, how stage-gate responsibilities are divided, and where the lead PM owns prompt or skill components. Clarify the three-office-days schedule and listed office options if relevant.

Availability last checked: Nov 4, 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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