Lead AI Product Manager with Retirement & Wealth Domain

Teamware Solutions; Boston (MA); Hybrid; USD 120,000 - 160,000; Full time

Job summary

Teamware Solutions is seeking a Lead AI Product Manager to join our Boston office or Windsor, CT. This role requires extensive experience in managing AI/ML products, with a focus on retirement and wealth management domains. Responsibilities include defining product strategies, overseeing execution, and ensuring stakeholder alignment.

The ideal candidate will have a proven track record in product management within financial services and demonstrate fluency in AI technologies and compliance frameworks. Join us to shape innovative solutions for our clients.

Qualifications

• 8+ years of product management experience, with at least 4 years in AI/ML product roles.
• Experience shipping AI-powered products to production.
• Lead experience in defining product strategy and roadmap independently.

Responsibilities

• Define product strategy and roadmaps to align with stakeholders.
• Lead execution and delivery of AI-focused products.
• Oversee compliance and legal review processes for product launches.

Skills

Product management experience

AI/ML product roles experience

Ability to influence stakeholders

Education

Relevant professional credentials (CFP, CFA, etc.)

Tools

SQL

A/B testing

Job description

Lead AI Product Manager with Retirement & Wealth Domain

Boston, MA or Windsor, CT

We need candidate to work onsite from Day 1 (Onsite Hybrid)

Responsibilities

• Discovery & Specification
• Execution & Delivery
• Stakeholder Alignment

Experience

• 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.

PREFERRED QUALIFICATIONS

• 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.