Lead AI Product Manager

Voya Financial • Philadelphia, PA

3d

About The Position

Voya Financial is seeking a Lead AI Product Manager to define and drive the AI product strategy for its Digital and Wealth Management business. This senior individual contributor role involves owning the product vision, roadmap, and go-to-market execution for AI-powered experiences. The role requires deep technical expertise in LLM architectures, agentic system design, and AI evaluation frameworks, combined with strong business judgment for a fiduciary-regulated environment. The Lead AI Product Manager will write specifications for engineers, evaluate prototype quality, and be accountable for product impact on advisor behavior and participant retirement outcomes. This role is benchmarked against similar senior AI PM roles at major financial institutions.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Finance, Economics, Mathematics, or a related quantitative discipline required.
• 8+ years of product management experience.
• At least 4 years in AI/ML product roles at a technology company, fintech, or financial services firm.
• Demonstrated track record of shipping AI-powered products to production, owning the full lifecycle from discovery through measurable adoption.
• Lead or principal-level experience: defined product strategy and roadmap independently.
• Prior ownership of products in a regulated environment (financial services, healthcare, or similar).
• Experience navigating compliance and legal review as part of the standard product process.
• Experience influencing VP-and-above stakeholders without direct authority.
• LLM product experience: shipped at least one production feature using large language models (OpenAI GPT-4o, Anthropic Claude, Google Gemini, or equivalent); understands prompt engineering, system prompt design, context window management, and structured output extraction.
• RAG architecture fluency: can evaluate the quality of a RAG pipeline.
• Agentic AI product design: has designed or shipped features using agentic workflows (tool use, multi-step reasoning, agent orchestration via LangChain, LangGraph, Vertex AI Agent Builder, Copilot Studio, or equivalent).
• Model evaluation and metrics: can define evaluation frameworks for AI outputs; understands precision/recall, ROC-AUC, hallucination rates, and task-specific quality metrics.
• Data fluency: comfortable interrogating SQL, reviewing data pipeline design, and forming hypotheses from participant behavioral data.
• AI tooling in practice: uses AI coding assistants (GitHub Copilot, Claude Code, Cursor, or equivalent) and agentic tools daily.
• API and system awareness: can read a technical architecture diagram, understand latency/reliability constraints, and write specs that account for engineering realities.
• Experimentation: A/B test design, cohort analysis, statistical significance, and shadow deployment patterns for AI features in production.
• Working fluency in Defined Contribution Plans (401(k), 403(b), 457 mechanics; contribution limits and catch-up provisions; employer match and vesting design; recordkeeper/TPA/plan sponsor ecosystem; QDIA rules; plan document fundamentals).
• Working fluency in ERISA & Fiduciary Standards (ERISA prudence and loyalty requirements; functional fiduciary standard and prohibited transactions; how AI-generated outputs must be structured to support fiduciary decision-making; DOL guidance on AI use in retirement plan contexts).
• Working fluency in the 2026 Regulatory Landscape (SECURE 2.0 provisions; the April 2026 interagency model risk management guidance superseding SR 11-7; evolving DOL fiduciary rule).
• Working fluency in Participant Behavior & Retirement Readiness (behavioral finance drivers of savings inertia; retirement income adequacy frameworks; auto-enrollment and escalation research; decumulation and guaranteed income strategies).
• Working fluency in Investment Products (Target-date fund construction and glide paths; managed account structures and fee models; model portfolio construction; how investment advice flows to participants).
• Working fluency in Advisor & Plan Sponsor Dynamics (Advisor business models; plan sponsor decision-making and governance committee structures; competitive recordkeeper landscape; how AI advisor copilots are being deployed).

Nice To Haves

• MBA or Master’s in a quantitative or financial field preferred.
• CFP, CFA (or candidate), CEBS, CRPS, or ASPPA credentials (QKA, QPA).
• Direct experience at a retirement recordkeeper, asset manager, RIA platform, or retirement-focused fintech in a product or strategy role.
• Familiarity with the 2026 interagency model risk management framework and its practical application to GenAI and agentic systems in a regulated financial institution.
• Experience with voice-of-customer research at scale: in-product feedback loops, NPS analysis, longitudinal participant cohort studies.
• Hands-on experience with MCP (Model Context Protocol) integrations or multi-agent system product design.
• History of building 0→1 AI products in an innovation lab or startup-within-a-large-institution context.

Responsibilities

• Define and maintain a 12–18 month AI product roadmap across participant engagement, advisor enablement, plan administration, and investment management, with clear prioritization rationale and measurable milestones.
• Identify and size AI opportunities using participant behavioral data, advisor workflow research, and competitive analysis; build business cases for leadership investment decisions.
• Define outcome-oriented success metrics (e.g., retirement readiness improvement, advisor engagement rates, plan sponsor NPS, administrative cost reduction) and own accountability for these metrics post-launch.
• Evaluate emerging AI capabilities (e.g., agentic frameworks, multimodal models, MCP integrations) and make principled decisions about their production readiness for a regulated retirement context.
• Lead structured discovery with advisors, plan administrators, plan sponsors, and participants, translating complex financial domain problems into crisp product specifications.
• Write PRDs and technical specifications for an AI engineering team, defining acceptance criteria that account for model quality thresholds, latency targets, and fiduciary output constraints.
• Maintain fluency in the participant retirement lifecycle (enrollment, contribution optimization, investment selection, pre-retirement income planning, and decumulation) to identify AI intervention points.
• Partner daily with AI engineers, evaluating RAG pipeline quality, reviewing agent behavior, and making real-time trade-off decisions.
• Drive rapid prototype cycles, using working software to earn stakeholder trust.
• Coordinate AI product launches with compliance, legal, and model risk teams, ensuring AI outputs meet fiduciary standards and regulatory guidance.
• Own go-to-market execution, including advisor enablement materials, plan sponsor communication, participant rollout sequencing, and adoption measurement.
• Build trusted relationships with senior stakeholders across business units, influencing without authority.
• Communicate AI product strategy and trade-offs clearly to diverse audiences.
• Represent Voya’s AI product vision with external stakeholders where appropriate.

Benefits

• Health, dental, vision and life insurance plans
• 401(k) Savings plan – with generous company matching contributions (up to 6%)
• Voya Retirement Plan – employer paid cash balance retirement plan (4%)
• Tuition reimbursement up to $5,250/year
• Paid time off – including 20 days paid time off, nine paid company holidays and a flexible Diversity Celebration Day.
• Paid volunteer time — 40 hours per calendar year

What This Job Offers

• Job Type: Full-time
• Career Level: Senior