AI Governance Lead

M&T Bank•Buffalo, NY

1d•$123,600 - $206,000

About The Position

The AI Governance Lead owns the design and execution of the enterprise AI governance program. The role develops standards, policies, and procedures governing the lifecycle of AI use cases and models, from initial idea through production. It structures a governance roadmap covering intake, classification, value assessment, and control design; establishes use case and model governance functions; defines the organization’s target state for AI governance; and leads an implementation plan with monitoring that is prioritized alongside engineering delivery timelines. The role also defines the governance function’s team structure and operating model, establishes responsibilities across governance, engineering, legal, risk, and business stakeholders, and creates program monitoring, reporting, and review processes.

Requirements

Combined minimum of 10 years of higher education and/or operational, business analytics, or systems development experience.
Demonstrated experience building a governance framework or program from the ground up in a large or complex organization.
Working knowledge of model risk management practices, such as SR 11-7 or equivalent, and their application to AI/ML systems.
Experience designing intake, classification, and risk assessment processes for technology or data initiatives.
Experience working cross-functionally with engineering, legal, compliance, and business teams.
Strong written communication skills, including the ability to produce policy documents, standards, and executive-level reporting.
Bachelor’s degree in a relevant field such as risk, business, computer science, or law; an advanced degree is a plus but is not required in place of experience.
Ability to manage stakeholders across technical and non-technical audiences.
Program and project management skills, including roadmap building, milestone tracking, and prioritization.
Working knowledge of the ML lifecycle and model development practices.
Data literacy sufficient to read model documentation, validation reports, and technical risk assessments.
Facilitation and negotiation skills, particularly in balancing governance rigor with engineering velocity.

Nice To Haves

Direct experience applying AI-specific regulatory frameworks such as the EU AI Act, NIST AI RMF, or ISO/IEC 42001.
Prior experience establishing or scaling a governance team, including hiring and role design.
Familiarity with MLOps and model deployment pipelines sufficient to design controls that do not bottleneck engineering.
Experience with generative AI governance, including prompt and data leakage risk, hallucination controls, and third-party LLM vendor risk.
Certifications such as CRISC, CISA, or AI governance-specific credentials.
Experience in a regulated industry such as financial services, healthcare, or insurance.
Advanced degree.

Responsibilities

Author and maintain AI governance standards, policies, and procedures covering the full lifecycle of AI use cases and models.
Design a governance roadmap organized into intake, classification, value assessment, and control design stages.
Define risk tiers and proportional controls for AI use cases.
Build an intake process for new AI use cases, including request submission, information capture, and review responsibilities.
Establish classification criteria covering risk level, data sensitivity, regulatory exposure, and business criticality.
Design a value assessment methodology to prioritize use cases against cost, risk, and business impact.
Define documentation, testing, sign-off, and monitoring requirements for each classification tier.
Establish model risk management practices for validation, documentation, versioning, and approval gates.
Define requirements for model cards, testing evidence, bias and fairness checks, and performance monitoring.
Set standards for vetting third-party and open-source models.
Define the target state for AI governance and expectations for the function.
Design the governance team structure, roles, and operating model.
Set RACI across governance, engineering, legal, risk, and business stakeholders.
Build a phased implementation plan with milestones and owners.
Prioritize governance rollout in sequence with engineering platform and architecture delivery.
Establish monitoring and reporting, including KPIs, compliance tracking, exception handling, and escalation paths.
Conduct periodic reviews of governance program effectiveness and adjust the program as needed.