Lead AI Product Manager with Retirement & Wealth Domain

Product Manager, AI/MLResponsibilities:Product Strategy & RoadmapDiscovery & SpecificationExecution & DeliveryStakeholder AlignmentExperience:• 8+ years of product management experience, with 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, not just executed against someone else’s vision.• 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.AI & Technical Fluency — Required and Evaluated:Evaluated rigorously. Candidates should expect to demonstrate these in the interview process, not just claim them on a resume.• 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 — chunking strategy, embedding model selection, retrieval precision/recall trade-offs, re-ranking logic, and hallucination mitigation. Does not need to implement but must be able to interrogate.• 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); understands where agents fail and how those failures affect fiduciary use cases specifically.• Model evaluation and metrics: can define evaluation frameworks for AI outputs; understands precision/recall, ROC-AUC, hallucination rates, and task-specific quality metrics; able to review an LLM eval suite and assess whether it covers the right failure modes for a retirement context.• Data fluency: comfortable interrogating SQL, reviewing data pipeline design, and forming hypotheses from participant behavioral data without requiring a data analyst to translate.• AI tooling in practice: uses AI coding assistants (GitHub Copilot, Claude Code, Cursor, or equivalent) and agentic tools daily — this team builds with these tools, not about them.• API and system awareness: can read a technical architecture diagram, understand latency/reliability constraints, and write specs that account for engineering realities including model serving costs and token limits.• Experimentation: A/B test design, cohort analysis, statistical significance, and shadow deployment patterns for AI features in production.Retirement & Wealth Domain— Mandatory Required:• 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.• ERISA & Fiduciary Standards: ERISA prudence and loyalty requirements; functional fiduciary standard and prohibited transactions; how AI-generated outputs must be structured to support — not replace — fiduciary decision-making; DOL guidance on AI use in retirement plan contexts.• 2026 Regulatory Landscape: SECURE 2.0 provisions (auto-enrollment, RMD changes, catch-up rules); the April 2026 interagency model risk management guidance superseding SR 11-7 — including its principles-based approach to materiality tiering and proportional controls for AI and agentic systems; evolving DOL fiduciary rule.• Participant Behavior & Retirement Readiness: Behavioral finance drivers of savings inertia; retirement income adequacy frameworks; auto-enrollment and escalation research; decumulation and guaranteed income strategies (relevant to SECURE 2.0 lifetime income provisions).• Investment Products: Target-date fund construction and glide paths; managed account structures and fee models; model portfolio construction; how investment advice flows to participants in a qualified plan context.• Advisor & Plan Sponsor Dynamics: Advisor business models (RIA, broker-dealer, captive); plan sponsor decision-making and governance committee structures; competitive recordkeeper landscape; how AI advisor copilots are being deployed at Morgan Stanley, JPMorgan, and peer firms.