Seven Seven
Principal Product Manager, AI
Principal Product Manager defines Humana’s AI-first product strategy, prototypes AI solutions with agentic tools, and drives portfolio delivery through governance, evals, and cross-functional leadership.
- Compensation
- Not disclosed
- Location
- New York, New York, United States
- Work setup
- Not specified
- Seniority
- Principal
- Employment
- Not specified
- Experience
- 10+ years
- Category
- Product
- Posted
Skills
Must have
- 10+ years of product or related experience, with AI/ML or GenAI products shipped to production
- Portfolio-level impact: strategy and delivery across multiple products or a major domain
- Hands-on AI tool usage, prototyping, and understanding of trade-offs (context windows, latency, cost, hallucination, RAG vs fine-tuning)
- Evaluation expertise: built evals (golden sets, ground truth, offline/live evals) and used precision/recall metrics
- Systems thinking: build reusable infrastructure to prevent classes of problems
- Fluent communication between business and technical concepts (business cases, ROI, roadmaps)
- Deep working understanding of modern AI/GenAI (LLMs, agents, RAG, prompt/harness engineering, evals)
- First-principles thinking, strong prioritization, and decision-making with incomplete information
Nice to have
- 12+ years of product experience or equivalent AI-native building in a large enterprise
- Experience in healthcare, insurance, or another regulated industry
- Publicly demonstrated point of view on agentic AI, evals, and AI-native product development
- Hands-on experience with agent frameworks, GraphRAG/knowledge graphs, and reusable skills/plugins
- Experience defining new AI product categories or cross-organization AI standards/playbooks
- Familiarity with responsible AI, bias mitigation, and compliance with sensitive data
- Degree in computer science or engineering, or equivalent experience
Full description
Description
As Humana continues its transformation into an AI-first enterprise, we are looking for a hands-on, technically deep Principal Product Manager to define strategy and drive delivery across a portfolio of AI products. You will set the standard for how we build AI at Humana, and personally go deep, from understanding a business challenge, to prototyping an AI solution, to proving its value, to delivering it through Humana's enterprise AI governance process. You will lead through influence, making engineers, data scientists, designers, product managers, and business leaders more effective.
What you will own
- Portfolio strategy and vision: Define the multi-year vision and strategy for a portfolio of AI products and capabilities. Prioritize across competing product areas and adapt the portfolio as the model landscape and the business change. Set the north-star and the long-term roadmap, and reason from first principles when the problem, opportunity, and strategy are not yet defined.
- Business discovery at scale: Partner with business-unit and enterprise leaders to map their operations end to end. Turn ambiguous, high-stakes problems into a slate of AI use cases, each with a clear business case, and sequence them into a portfolio.
- Hands-on prototyping: Build working prototypes and demos with agentic coding tools (Claude Code, Cursor, OpenAI Codex) to prove feasibility and value before a full team is committed. Stay close enough to the technology to make credible technical calls.
- Requirements and scoping: Turn validated use cases into buildable requirements: user stories, acceptance criteria, edge cases, and success metrics. Shape the end-to-end user experience with design, because adoption depends on usability.
- Delivery and execution: Drive the build from prototype to production across your portfolio. Sequence the work, make scope and tradeoff calls as models and constraints shift, unblock teams, and keep momentum from first demo through launch and iteration.
- Evaluation and quality standards: Define how quality is measured. Build golden sets, error budgets, and eval frameworks; run offline and production evals; choose the right metrics (precision, recall, task success, hallucination rate); apply LLM-as-judge where it has been validated. Turn results into launch decisions and leadership readouts.
- Solution architecture: Partner with engineering on high-level architecture and feasibility across the portfolio, while owning the context engineering and skill-creation components of the AI harness and setting the reusable patterns others build on.
- Cross-organizational leadership: Lead engineers, data scientists, designers, product managers, and business partners without direct authority. Influence VP-level organizations through written narratives, business cases, and executive forums. Author the playbooks, skills, and solution patterns for building AI at Humana.