Lead AI Product Manager with Retirement & Wealth Domain

Lead AI Product Manager with Retirement & Wealth Domain

Boston, MA or Windsor, CT

Type : Contract

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

Responsibilities:

• Product Strategy & Roadmap
• 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.

Tekshapers is an equal opportunity employer and will consider all applications without regards to race, sex, age, color, religion, national origin, veteran status, disability, sexual orientation, gender identity, genetic information or any characteristic protected by law.