AI Risk Oversight, Director

BlackRock•New York, NY

1d•$210,000 - $270,000•Hybrid

About The Position

BlackRock is seeking a Director to lead AI Risk Oversight within its Enterprise Risk Management function in Risk & Quantitative Analysis (RQA). RQA provides independent oversight of fiduciary and enterprise risks. As a second line of defense, ERM oversees risks affecting the corporate entity and helps ensure that senior management has established effective controls to protect clients and the firm while supporting business goals within the firm’s risk tolerance. The role provides independent oversight and challenge across the end-to-end AI lifecycle, including how AI systems are built and governed, where models and AI services operate, how they access firm data and systems, and how they are deployed and monitored in production. Its scope spans AI model and deployment risks—including agentic AI—as well as AI architecture and infrastructure, access management, third-party enablement and native AI use, cybersecurity dependencies, production monitoring, and compute and inference risk. The Director will partner with Model Risk Management, Technology and Cyber Risk, Third Party Risk, Information Security, Engineering, and first-line AI owners to establish a holistic firm-wide view of AI risk. The role combines technical understanding of modern AI systems with risk and governance expertise, and requires building a credible AI risk capability within RQA, challenging senior leaders, and communicating complex risks to executive and board-level audiences.

Requirements

Enterprise experience in model risk, AI/ML governance, technology risk, enterprise risk, engineering risk, model implementation, or a closely related control discipline.
Strong understanding of the AI/ML lifecycle, including model development, validation, integration, deployment, inference, monitoring, and production operations.
Experience with model governance and model implementation, and ability to distinguish traditional model validation from broader technology and operational risks of enterprise AI deployment.
Strong understanding of modern AI architecture and infrastructure, including model APIs, data and inference pipelines, cloud environments, compute/GPU infrastructure, access management, and production deployment patterns.
Understanding of generative and agentic AI risks, including hallucination, reliability, data access, third-party models, and AI use in automated or agentic workflows.
Experience assessing third-party technology or AI risk, including dependencies involving external models, platforms, cloud infrastructure, and AI service providers.
Ability to assess technical and operational controls and provide credible independent challenge to senior engineers, AI practitioners, and business leaders.
Demonstrated experience developing risk metrics, indicators, and senior management reporting that translates complex technical issues into clear risk assessments.
Excellent judgment and critical reasoning, including the ability to distinguish theoretical AI risks from material risks in real-world enterprise production environments.
Strong communication and influencing skills, with the ability to work credibly with senior technology, risk, and business executives.
Bachelor’s degree in computer science, engineering, mathematics, statistics, finance, risk management, or a related discipline.

Nice To Haves

Experience in financial services or another highly regulated environment.
Familiarity with established and emerging AI and technology risk frameworks, such as the NIST AI Risk Management Framework and NIST Cybersecurity Framework, and with model risk management principles.
Advanced degree or relevant professional certification.

Responsibilities

Provide independent oversight, review, and challenge of AI risk across the firm, assessing how AI capabilities are designed, governed, implemented, deployed, and monitored.
Hold first-line owners accountable for managing AI risks within the firm’s risk tolerance.
Assess AI technology architecture and operating environments, including model and data pipelines, access and entitlements, production deployment, cloud and compute infrastructure, GPU utilization, and cybersecurity dependencies.
Oversee risks from AI models operating in production, including human oversight and controls around generative and agentic AI, in partnership with Model Risk Management.
Partner with Third Party Risk to assess end-to-end risks from external AI models, platforms, and infrastructure, including vendor dependencies, data and system access, model changes, cloud and compute concentration, and controls from selection through production deployment.
Provide independent oversight of AI-related incidents and control issues; challenge root-cause analysis and remediation, identify systemic or emerging themes, and ensure material risks and unresolved issues are escalated.
Develop enterprise AI risk metrics, indicators, and senior-level reporting that translate technical risks into clear assessments of residual risk for senior management, committees, and boards.
Connect AI risk oversight across Model Risk Management, Technology and Cyber Risk, Third Party Risk, Data Risk, Legal and Compliance, Privacy, and business technology teams.
Build and lead an AI risk capability within RQA, challenge senior AI, engineering, and business leaders, and communicate complex AI risks clearly to executive and board-level audiences.