Product Manager, Specialist – Generative AI

The Vanguard Group•Charlotte, PA

8h•Hybrid

About The Position

The Product Manager, Specialist – Generative AI will lead the evolution and scale of this AI-powered conversational experience. This role owns end-to-end for multiple domains, from problem discovery and experimentation through pilot, rollout, and governance. The Specialist will identify high-impact AI opportunities (domain), define strategy, establish evaluation and monitoring frameworks, and design human oversight mechanisms appropriate for a regulated financial environment. The role requires strong product fundamentals combined with a working knowledge of Generative AI principles like LLM’s, SLM’s, conversational design, evaluation techniques, and AI safety considerations. This individual treat trust, safety, and continuous learning as core product features, not afterthoughts. This AI assistant serves as the foundation for Vanguard’s broader AI-powered client experience strategy.

Requirements

5 years related work experience.
Product Management experience utilizing GenAI
Undergraduate degree or equivalent combination of training and experience required.

Nice To Haves

2 years of leading large cross-functional teams on major organizational projects preferred.
Graduate degree preferred.

Responsibilities

Lead product management efforts across one or more strategic domains.
Identify and prioritize high-impact AI use cases that expand the AI assistant’s value to clients and internal stakeholders.
Work with senior product leadership along with peers leading other domain work to evolve its product vision, roadmap, and measurable success outcomes including quality, trust, adoption, and business impact.
Drive initiatives through discovery, feasibility assessment, experimentation, pilot, and scale.
Validate that Generative AI is the appropriate solution versus deterministic or rules-based approaches.
Define measurable quality bars and acceptance criteria for it’s outputs, including accuracy, completeness, compliance, explainability, and latency.
Establish evaluations for your domain area including ground truth creation, annotation strategies, precision and recall trade-offs, and continuous model monitoring.
Partner with data science and engineering to track model performance, drift, hallucinations, confidence thresholds, and flagged interactions.
Translate model behavior and evaluation results into actionable product decisions and iteration plans.
Continue to work with other product teams to manage human-in-the-loop systems, including review workflows, escalation paths, and confidence gating.
Collaborate closely with Legal, Compliance, Risk, and Data Governance to ensure appropriate disclosures, guardrails, and privacy controls are embedded from inception.
Define acceptable versus unacceptable use cases and ensure outputs align with enterprise standards for safety, transparency, and explainability.
Partner with UX and prompt engineers to design this AI assistant’s conversational experience, reducing ambiguity and supporting recovery from misunderstandings.
Balance accuracy, latency, and client trust in its conversational flows.
Leverage user feedback, flagged conversations, and behavioral metrics to continuously refine the AI assistant’s experience.
Work closely with engineering, data science, UX, legal, compliance, operations, and business stakeholders to align on priorities and deliverables.
Translate strategy into clear requirements, experiments, and execution plans.
Participate in ongoing planning, departmental prioritization activities, and enterprise AI strategy discussions as required.