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Senior Vice President, AI Governance - Safety & Observability
senior · Risk & Compliance
BNY is seeking a Senior Vice President, AI Governance - Safety & Observability to help mature the firm’s enterprise AI governance capabilities as AI adoption scales across platforms, workflows, and business processes. This role will support the development and execution of governance standards, controls, monitoring routines, reporting, and operating practices that help ensure AI capabilities are responsibly managed across their lifecycle. The role will be located in New York, NY.
The role will work across AI Hub, product, engineering, data, model risk, technology risk, cybersecurity, compliance, legal, audit, and business teams to improve governance consistency, reduce risk, simplify approval pathways, and enable better end-to-end visibility across the AI environment. The ideal candidate will bring experience in AI governance, technology risk, model lifecycle oversight, controls, data governance, or enterprise technology delivery, with the ability to translate governance expectations into practical operating routines, workflow automations, and measurable outcomes.
In this role, you’ll make an impact in the following ways:
- Build AI observability practices: Support monitoring standards and reporting for AI systems, including usage, output quality, safety signals, policy adherence, exceptions, incidents, performance, and operational health.
- Advance enterprise AI governance practices: Support the build-out of observability tooling, process, workflows, frameworks, standards, controls, dashboards, and operating routines that improve visibility into AI use, risks, ownership, incidents, and control adherence.
- Accelerate AI solution selection and deployment: Help product and engineering teams assess AI solutions, understand approval requirements, prepare governance evidence, and move from evaluation to production with greater speed and consistency.
- Create governance-enablement agents and workflows: Develop or support AI-enabled agents, workflow automations, templates, and decision-support tools that streamline intake, triage, documentation, review readiness, approval routing, and control evidence collection.
- Mature AI safety capabilities: Help define and operationalize safety expectations across AI use cases, including responsible use, human oversight, fallback handling, escalation paths, documentation, and safe operation of AI-enabled capabilities.
- Support end-to-end AI lifecycle governance: Help define and execute governance checkpoints across AI use-case intake, design, development, testing, deployment, monitoring, and ongoing production use.
- Streamline governance and approval processes: Partner with product, engineering, risk, and control teams to reduce duplication, clarify ownership, simplify review steps, and improve consistency across AI initiatives.
- Enable governance transparency: Create and maintain executive-ready reporting, metrics, KRIs, KPIs, and dashboards that provide visibility into AI portfolio health, governance status, control gaps, and remediation progress.
- Partner across risk and control functions: Work with model risk, technology risk, cybersecurity, compliance, legal, privacy, audit, and data governance teams to align AI governance practices with enterprise standards.
- Support resiliency and incident readiness: Help define expectations for reliability, continuity, escalation, fallback procedures, issue management, and operational response for critical AI-enabled services.
- Advise delivery teams on governance readiness: Provide practical guidance to product and engineering teams on governance requirements, review readiness, control evidence, monitoring needs, and production preparedness.
- Prepare senior stakeholder materials: Develop clear narratives, decision materials, status updates, and governance findings for senior leaders and cross-functional forums.
To be successful in this role, we’re seeking the following:
- Bachelor’s degree in computer science, information technology, mathematics/statistics or the equivalent combination of education and experience is required.
- 7-10 years of experience in Observability, Engineering, Monitoring, Metrics, systemic behavioral analytics or related roles.
- Experience supporting or leading observability and systemic metrics activities across technology, data, analytics, AI, automation, applications, or model-enabled capabilities.
- Strong understanding of AI lifecycle management, design, testing, monitoring, documentation, approval workflows, and production oversight.
- Experience creating valuable observability, monitoring, risk reporting, operational metrics, control dashboards, issue management, or production readiness practices.
- Experience mining detail with observability tools such as Splunk, Dynatrace, Arize AX / Phoenix, Langfuse, Langsmith, Braintrust, Graphana, and data extraction techniques from various sources
- Ability to translate governance, risk, and control expectations into practical requirements, tools, templates, workflows, and approval pathways that product and engineering teams can execute.
- Familiarity with GenAI agents, decision-support tools, or automation approaches that improve governance efficiency is preferred.
- Strong analytical and problem-solving skills, with the ability to identify risks, assess root causes, define remediation plans, and track progress.
- Strong communication skills, including the ability to prepare executive-level materials and explain complex AI governance topics clearly to technical and non-technical stakeholders.
- Demonstrated ability to work across matrixed teams and influence stakeholders across product, engineering, risk, compliance, audit, and business functions.
Preferred qualifications:
- Experience in financial services, banking, asset servicing, asset management, payments, markets, or another regulated industry.
- Exposure to GenAI, LLM applications, agentic workflows, model lifecycle management, or AI platform governance.
- Familiarity with responsible AI principles, AI safety practices, model evaluation, output-quality monitoring, incident management, risk taxonomies, and control frameworks.
- Experience using or designing dashboards, KRIs, KPIs, issue trackers, governance repositories, workflow tools, approval workflows, or enterprise reporting routines.