Artificial Intelligence Oversight and Governance, Vice President

Morgan Stanley•Town/Village of Harrison, NY

2d•$110,000 - $190,000

About The Position

Artificial Intelligence Oversight and Governance, Vice President Wealth Management Platforms Purchase, NY We are seeking an innovative, driven professional to join us as Vice President, Artificial Intelligence Oversight and Governance, within our Product organization and part of Wealth Management Platforms team. This role will be accountable for end-to-end governance of AI-enabled product capabilities—especially agentic AI use cases, agent orchestration, and Retrieval-Augmented Generation (RAG) implementations—ensuring new AI use cases are properly intake-managed, risk-reviewed, prioritized by value, aligned to governance frameworks, and monitored post-production. This PM Lead will work cross-functionally with Product, Engineering, Data/ML, Security, Risk/Compliance, Legal/Privacy, and platform teams to operationalize trustworthy, secure, and compliant AI practices at scale—while enabling teams to deliver high-value AI agents quickly and safely. The program will be grounded in widely adopted AI risk and governance frameworks and will incorporate LLM/agentic security considerations. About Morgan Stanley Morgan Stanley is a leading global financial services firm providing a wide range of investment banking, securities, investment management and wealth management services. The Firm’s employees serve clients worldwide including corporations, governments and individuals from more than 1,200 offices in 43 countries. Morgan Stanley is committed to helping its employees build meaningful careers and we strive to be a place for people to learn, achieve and grow. Department Overview In the Wealth Management division, we help people, businesses and institutions build, preserve, and manage wealth so they can pursue their financial goals Wealth Management (WM) Platforms manages industry-leading platforms, across all WM channels and client segments, to provide a unified digital experience, unlock growth, and deliver efficiencies for Advisors, Clients, and Institutions. WM Platforms consists of ten sub-teams including: Field Experience & Platforms, Digital Client Experience & Platforms, Workplace Platforms, Automation & Workflow Solutions, Digital Trading & Investing, Generative AI, UX Design & Research, Strategy & Execution. Business Control & Support, and the Chief Operating Office. We know a lot about investing and are certain that there’s no better investment a company can make than in its employees. People don’t just drive a company; they are the company. When people work at their best, companies do too. Morgan Stanley at Work, a division of Morgan Stanley Wealth Management, provides workplace financial solutions that build employee financial confidence, foster loyalty and help our corporate clients attract and retain top talent. Our end-to-end offering spans Equity, Financial Wellness and Retirement Solutions. Plus, we provide all employees with Financial Empowerment, so they have the knowledge, tools and support needed to make the most of their workplace financial benefits. Within Morgan Stanley, the Product team is responsible for a comprehensive B2B, B2C and B2B2C offering designed to help companies and their employees meet their financial goals, through trusted and modern workplace financial digital solutions, including Equity Edge Online by Morgan Stanley, ETrade.com, Shareworks by Morgan Stanley, and more. The product team spans across the U.S, and Internationally, including Canada and India

Requirements

7+ years of Product Management experience, including leading cross-functional platform or governance programs.
Demonstrated experience delivering AI/ML or LLM-enabled product capabilities in production.
Hands-on familiarity with agentic AI patterns and orchestration concepts (tools/actions, planning/execution loops, guardrails, human-in-the-loop design).
Proven track record working across engineering and data/ML teams to ship complex systems with measurable outcomes.
Strong ability to translate risk, controls, and policy requirements into product requirements and delivery workflows.
Sets and carries out objectives, and fosters a culture of performance, continuous improvement.
Excellent partnering skills; proven ability to collaborate with various levels of employees.
Enjoys building initiatives from the ground-up and delivering on complex projects.
Excellent communication skills and a proven ability to develop rapport and credibility across the organization, promote ideas and proposals persuasively.
Expert proficiency with Excel, Word, Visio and PowerPoint required.
Positive contributor to our team’s culture.

Nice To Haves

Experience operationalizing or mapping to AI governance/risk frameworks.
Experience partnering with Security and Risk teams on LLM/agentic security risks (e.g., prompt injection, sensitive info disclosure, access controls).
Familiarity with model evaluation approaches and with governance artifacts needed for audits.
Experience with regulated environments (financial services, preferred).
Previous experience working with large or multi-national companies helpful

Responsibilities

AI Use Case Intake, Triage, and Prioritization, including establish and operate an AI use case intake workflow (new agents, agent enhancements, model changes, new RAG inputs, new tools/actions, new prompts/policies).
Build and maintain a prioritized roadmap of AI agent opportunities, ensuring delivery focus stays on the highest-value agents while appropriately accounting for risk and governance requirements.
Governance-by-Design for Agentic AI RAG inputs, including partner with engineering and data teams to embed governance controls into the AI delivery lifecycle (requirements, build, test, launch, monitor).
Define oversight patterns for agent orchestration (tool access, action permissions, escalation/human-in-the-loop checkpoints, auditability, fallback behavior).
Ensure we are aligned to firmwide AI governance and Data governance standards.
Risk Management, Control Alignment, and Review Cadence, including operationalize AI risk management practices within our organization aligned to recognized firmwide frameworks.
Align program design to AI management system concepts (policy/objectives, lifecycle controls, monitoring/performance evaluation, continual improvement).
Ensure agentic/LLM security risks are addressed (e.g., prompt injection, insecure output handling, sensitive info disclosure, more), and integrate mitigations into product requirements and acceptance criteria.
Production Readiness and Post-Launch Monitoring, including define and run an AI production readiness process that includes: evaluation sign-off, privacy/security review, model/tooling change management, and operational runbooks.
Establish post-launch monitoring requirements: quality metrics, safety metrics, drift signals, abuse signals, incident response playbooks, and periodic re-review.
Supporting a recurring governance forum (or “AI review board” operating model), ensuring new use cases are reviewed, decisions are recorded, and exceptions are tracked with time-bound remediation.
Stakeholder Management and Operating Model, including serve as the connective tissue across Product, Engineering, Data/ML, Security, Privacy, Legal, Compliance, and Platform teams.
Clarify roles and decision rights (who approves what, when), reduce friction in delivery, and ensure teams can move fast without bypassing controls.
Produce clear executive-ready reporting: roadmap, risk posture, incidents, governance throughput (cycle time), and outcomes.