Engineering Director - Head of AI Engineering

Overview

The Director of AI Engineering manages the activities and strategic direction of multiple departments, including indirectly managing diverse Engineering and Architecture teams and enterprise AI Engineering platform teams. This role is responsible for budgets across multiple cost centers spanning concurrent development streams and provides both long- and short-term strategic direction in partnership with Engineering, Architecture, and Technology senior management.

The Director serves as a primary contact to Divisional peers, Management Group members, Bank-wide senior management, and business partners. As a technology leader and industry expert, this role focuses on establishing AI (including Generative AI) as a governed, scalable, and value-driven enterprise capability embedded within modern software delivery.

This position leads the strategic direction, operating model, and execution of enterprise AI Engineering, GenAI platforms, and AI-enabled SDLC capabilities. The role is accountable for defining and operationalizing an end-to-end AI operating model spanning platform engineering, governance, security, and delivery, enabling consistent enterprise adoption while meeting risk, compliance, and regulatory requirements.

Primary Responsibilities

Divisional Strategy, Leadership & Operating Model

• Build Technology divisional strategy across departments in partnership with peers and Technology senior management, positioning AI and GenAI as core enterprise engineering capabilities

• Define and execute enterprise AI strategy and adoption roadmaps aligned to business outcomes and measurable value realization

• Establish and operationalize an end-to-end AI operating model spanning platform engineering, delivery execution, governance, and risk

• Partner with senior leaders outside of Technology to build strong client relationships supporting delivery of complex technology initiatives, including AI-enabled transformation

• Serve as a primary interface to Technology leadership, business partners, and risk stakeholders, ensuring alignment, performance, and organizational development across AI engineering and platform teams

AI Platform & Engineering Enablement (GenAI, LLMs, AI-Enabled DevEx)

• Lead enterprise AI platform capabilities including GenAI platforms, LLM integration patterns, model and tool onboarding, and AI-enabled developer experience

• Drive automation-first SDLC transformation by embedding AI into development, testing, security, quality, and delivery workflows

• Oversee standardization of repositories, CI/CD pipelines, and engineering tooling to enable scale, reuse, and consistent governance

• Ensure integration of AI capabilities across enterprise platforms and delivery pipelines in alignment with Divisional standards and long-range plans

• Monitor industry trends and vendor offerings relevant to AI engineering and SDLC modernization and initiate change when appropriate

Delivery Execution, Adoption & Change Management

• Oversee systems development activities of assigned teams, including staff assignments and ensuring timely completion within budget

• Define and manage AI delivery roadmaps, adoption models, success metrics, dependencies, milestones, and KPI/OKR reporting

• Implement maturity-based adoption models (e.g., sandbox, controlled pilot, production) to scale usage while maintaining appropriate guardrails

• Drive enterprise adoption, enablement, and change management to increase developer productivity and accelerate delivery outcomes

Governance, Risk, Compliance & Responsible AI

• Define and operationalize AI standards, policies, and guardrails that balance enablement with compliance through automated controls and documentation

• Partner with Risk, Security, and Compliance teams to embed controls and regulatory alignment into AI platforms, pipelines, and AI-enabled SDLC workflows

• Maintain internal control standards, including timely implementation of internal and external audit requirements and remediation of regulatory findings

• Ensure adherence to the Company’s risk and regulatory standards in alignment with the Company’s Risk Appetite

• Design, implement, maintain, and enhance internal controls to mitigate risk and escalate issues as appropriate

• Ensure activities comply with all Department and Technology standards, procedures, and documentation requirements

Metrics, Performance & Optimization

• Establish enterprise metrics for AI adoption, SDLC impact, platform performance, reliability, and value delivery

• Drive continuous improvement through data-driven insights and outcome-based reporting to senior management

• Optimize AI platform cost, licensing, utilization, and vendor spend across multiple cost centers

Technology, Vendor & Financial Management

• Manage multiple Technology departments with responsibility for financial, compliance, HR, and risk operations

• Lead vendor and product evaluations to ensure delivery of optimal solutions within available resources and budget

• Manage strategic vendor and professional relationships to remain current on AI trends, best practices, and emerging capabilities

People Leadership & Culture

• Exercise full managerial authority related to staffing, performance management, promotions, compensation, and terminations

• Build and lead enterprise AI engineering and platform organizations, establishing clear accountability across platform, delivery, and governance functions

• Promote an inclusive environment that supports belonging and reflects the M&T Bank brand

• Encourage teamwork and serve as a role model in leadership and collaboration

Scope of Responsibilities

• Oversees teams primarily composed of engineers, architects, Engineering Supervisors, Team Leads, Managers, and Senior Managers

• Includes enterprise AI engineering and platform teams enabling GenAI and LLM adoption and AI-enabled SDLC capabilities

Supervisory / Managerial Responsibilities

• 50–100 employees (direct and indirect), including potential contractor resources aligned to delivery and platform needs

Education, Skills, and Experience Required

• A combined minimum of 15 years of higher education and/or work experience

  • Minimum 4 years of engineering and/or architecture experience

  • Minimum 11 years of large technology management or program leadership experience, including people management

• Proficiency with project management, word processing, and spreadsheet applications

• Ability to manage multiple complex initiatives simultaneously

• Experience with large system enhancements, conversions, and production issue resolution

• Complete understanding of the system development life cycle

• Strong problem-solving, analytical, and decision-making skills

• Familiarity with application development platforms, software, and hardware

• Strong understanding of supported business domains and terminology

• Excellent written and verbal communication skills

• Confidence leading large, geographically distributed teams (100+ employees)

• Expertise presenting to senior management

• Demonstrated experience defining enterprise AI and GenAI strategies, operating models, and adoption roadmaps

• Extensive experience leading enterprise-scale AI engineering, GenAI/LLM platforms, and developer platforms

• Proven ability to drive cross-functional alignment across engineering, business, risk, security, and compliance

• Experience leading large-scale platform modernization and AI-enabled SDLC transformation initiatives

• Experience establishing AI governance guardrails, operational controls, audit readiness, and regulatory compliance

Education and Experience Preferred

• Master’s degree

• Minimum of 14 years of technology management or large program leadership experience

• Extensive application and product domain expertise within the technology area being led

• Subject matter expert understanding of supported applications and integrated systems

• Understanding of multiple business functions

• Proven mentoring and leadership capabilities across multiple management layers

• Extensive experience leading enterprise developer platforms or engineering enablement organizations

• Subject matter expertise in developer tooling, CI/CD, reliability, and quality engineering

• Strong expertise in GenAI and LLM platforms and enterprise adoption patterns

• Strong understanding of the Bank’s application framework and strategic objectives

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