Head of Forward Deployed Engineering (AI)

Overview

The Head of Forward Deployed Engineering (AI) leads a team of Forward Deployed Engineers responsible for partnering directly with business units to design, build, and deliver AI-powered solutions that address real-world business challenges. This leader oversees concurrent engagements across multiple business areas, ensuring solutions are delivered with speed, quality, security, and alignment to enterprise AI platform standards.

Serving as the primary connection between business stakeholders and the central AI Engineering organization, this role establishes engagement models, drives execution excellence, and ensures reusable, governed, and scalable AI capabilities are effectively deployed across the enterprise. The position is responsible for strategic team leadership, staffing, budget management, technical oversight, stakeholder engagement, and the ongoing development of engineering talent.

Primary Responsibilities

• Lead, mentor, and develop a team of Forward Deployed Engineers, establishing priorities across multiple concurrent business engagements and resolving resource allocation challenges.
• Serve as the technical and delivery owner for Forward Deployed Engineering engagements by partnering with business stakeholders to identify opportunities, scope AI use cases, define solution architectures, and ensure successful delivery.
• Manage and participate in consultations with business and technology leaders to analyze short- and long-term business requirements and recommend innovative AI-driven solutions that anticipate future needs.
• Establish and maintain strong partnerships with business units, technology teams, and executive stakeholders while acting as the primary escalation point for engagement-related issues.
• Ensure AI solutions developed within business units align with enterprise AI platform standards, governance requirements, architecture principles, and long-term technology strategies.
• Drive adoption and reuse of centralized AI services, platforms, tooling, and shared engineering components to avoid fragmented or one-off implementations.
• Serve as the voice of the field by identifying recurring business needs, platform gaps, tooling opportunities, and reusable capabilities that should be developed centrally.
• Define and continuously improve engagement standards, including intake processes, solution discovery, project scoping, delivery methodology, deployment practices, and post-launch support models.
• Establish and enforce engineering quality standards including code quality, testing, security, data protection, model governance, production readiness, and operational support requirements.
• Oversee multiple AI solution development initiatives and ensure timely delivery of projects across diverse business functions and stakeholder groups.
• Monitor industry trends, emerging technologies, AI advancements, and vendor capabilities to identify opportunities that improve business outcomes and engineering effectiveness.
• Research, evaluate, and recommend technologies, products, vendors, and engineering approaches that support the organization's AI strategy.
• Develop and manage departmental budgets, resource plans, vendor relationships, and staffing strategies to support organizational objectives.
• Provide regular reporting on engagement performance, delivery status, operational metrics, business impact, resource utilization, and strategic priorities to senior leadership.
• Exercise managerial authority regarding staffing, hiring, coaching, performance management, promotions, compensation recommendations, succession planning, and employee development.
• Foster an environment that promotes collaboration, innovation, continuous learning, belonging, and reflects the M&T Bank brand.
• Understand and adhere to the Company's risk and regulatory standards, policies, and controls in accordance with the Company's Risk Appetite. Design, implement, maintain, and enhance internal controls to mitigate risk on an ongoing basis. Identify risk-related issues needing escalation to management.
• Maintain M&T internal control standards, including timely implementation of internal and external audit findings together with any issues raised by external regulators, as applicable.
• Complete other related duties as assigned.

Scope of Responsibilities

Leads a team of Forward Deployed Engineers responsible for delivering AI solutions directly within business units while ensuring alignment with enterprise architecture, AI governance, engineering best practices, and platform strategy. Oversees multiple concurrent AI engagements spanning software engineering, machine learning, generative AI, solution architecture, and stakeholder delivery functions.

Acts as a key member of the AI Engineering leadership team, balancing business-specific delivery objectives with broader enterprise platform consistency, scalability, risk management, and technology strategy.

Supervisory/Managerial Responsibilities

Typically manages 10 to 20 employees, including senior engineers, technical leads, and other engineering professionals.

Education and Experience Required

• A combined minimum of 11 years' higher education and/or work experience, including a minimum of 4 years' engineering and/or architecture experience and 5 years' leadership experience including people management.
• Experience delivering software, data, machine learning, or AI solutions in close partnership with business and product stakeholders.
• Hands-on experience building and deploying production AI applications utilizing large language models (LLMs), retrieval-augmented generation (RAG), AI agents, model APIs, machine learning models, or related technologies.
• Experience managing multiple concurrent initiatives with competing stakeholder priorities and delivery timelines.
• Strong understanding of solution architecture, software engineering practices, application development methodologies, and production support models.
• Demonstrated ability to establish engineering standards and perform effective architecture, code, and design reviews.
• Proficiency with project management, collaboration, reporting, and productivity tools.
• Capable of leading large, complex technical initiatives across multiple teams and business areas.
• Excellent analytical, problem-solving, organizational, and decision-making skills.
• Excellent verbal and written communication skills, including experience presenting technical concepts to senior executives and non-technical audiences.
• Experience fostering collaboration, teamwork, and employee development while leading geographically distributed teams.
• Understanding of technical, operational, regulatory, and business impacts associated with AI-enabled solutions.

Education and Experience Preferred

• Bachelor's degree in Computer Science, Engineering, Data Science, Information Technology, or a related field.
• Experience leading Forward Deployed Engineering, Solutions Engineering, Professional Services Engineering, Customer Engineering, or similar field-facing engineering organizations.
• Experience partnering with centralized platform, architecture, governance, or engineering enablement functions.
• Experience building, scaling, or transforming a Forward Deployed Engineering organization.
• Experience delivering AI solutions within highly regulated industries such as financial services, healthcare, insurance, or similar environments.
• Strong knowledge of enterprise AI governance, model governance, use case intake processes, risk assessment frameworks, and responsible AI practices.
• Extensive experience with AI/ML platforms, generative AI technologies, cloud-native architectures, and production engineering practices.
• Demonstrated success developing engineering talent and building high-performing technical teams.
• Strong understanding of enterprise technology strategy, platform operating models, and organizational change management.
• Experience influencing senior stakeholders and driving strategic technology initiatives across large organizations.

Skills Required

• People leadership, coaching, hiring, and organizational development
• Applied AI and machine learning solution architecture
• Generative AI technologies including LLMs, RAG, agents, and model integrations
• Stakeholder management and executive communication
• Technical delivery leadership and program execution
• Resource planning and prioritization across concurrent engagements
• Software engineering best practices including testing, code review, CI/CD, and security fundamentals
• Enterprise architecture and platform alignment
• AI governance, risk management, and compliance awareness
• Strategic planning, budget management, and organizational leadership
• Ability to translate business challenges into scalable AI solutions
• Ability to translate field engagement patterns into platform capabilities and enterprise standards

Location