W3Global

W3Global

Lead AI Product Manager with Retirement & Wealth Domain

Boston, Massachusetts, USA · Contract

Sponsorship not specifiedDetected 64 days ago
GitSQLMachine LearningNLPLLMsRAGAgentic AILangGraphAI OrchestrationA/B TestingProduct ManagementProduct StrategyResearchCFA

About the role

  • Demonstrated track record of shipping AI-powered products to production - owning the full lifecycle from discovery through measurable adoption.
  • Experience influencing VP-and-above stakeholders without direct authority.
  • Candidates should expect to demonstrate these in the interview process, not just claim them on a resume.

Responsibilities

  • 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.
  • 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.
  • 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.
  • Experimentation: A/B test design, cohort analysis, statistical significance, and shadow deployment patterns for AI features in production.
  • History of building 0?1 AI products in an innovation lab or startup-within-a-large-institution context.
  • 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.
  • 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.

Requirements

  • Does not need to implement but must be able to interrogate.
  • Required and Evaluated Evaluated rigorously.
  • AI & Technical Fluency - Required and Evaluated Evaluated rigorously.

Nice to have

  • CFP, CFA (or candidate), CEBS, CRPS, or ASPPA credentials (QKA, QPA).
  • 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.

Benefits

  • 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.
  • Direct experience at a retirement recordkeeper, asset manager, RIA platform, or retirement-focused fintech in a product or strategy role.

Visa & Work Authorization

  • ), 457 mechanics; contribution limits and catch-up provisions; employer match and vesting design; recordkeeper/TPA/plan sponsor ecosystem; QDIA rules; plan document fundamentals
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This listing is sourced directly from W3Global's careers page and normalized into a canonical job model.