Manager, AI and ML Engineering

Manager, AI and ML Engineering

Job Description

The Manager, AI and ML Engineering is a leader in building high-performing teams that turn AI opportunities into production-ready solutions. This leader combines strong people leadership, technical judgment, and a bias for delivery to guide the design, development, evaluation, and continuous improvement of AI applications and services. Working closely with Product, Engineering, Enterprise Data, Architecture, Security, and business partners, the manager translates strategy into execution, develops talent, establishes engineering excellence, and helps scale responsible AI across the university. Reporting to the Director, Enterprise AI Platforms, this role leads the AI Engineering team and supports the Director in advancing engineering strategy, technical standards, delivery practices, and organizational priorities across the broader Enterprise AI Platforms organization.

Essential Functions and Responsibilities:

Leads, develops, and inspires a high-performing team of AI Engineers through clear expectations, coaching, accountability, and purposeful career development.

Translates AI strategy and organizational priorities into an actionable team roadmap, delivery plans, and measurable outcomes.

Leads the design, development, testing, evaluation, deployment, and continuous improvement of production AI applications and services.

Establishes and reinforces engineering standards, design patterns, development practices, and quality expectations that improve reliability, scalability, security, and developer productivity.

Partners with the Director, Enterprise AI Platforms, to shape AI Engineering strategy, technical priorities, organizational planning, and cross-team initiatives.

Drives delivery of complex, high-impact AI initiatives, balancing near-term business outcomes with long-term technical sustainability.

Partners closely with AI Platform Operations and other technology teams to ensure AI solutions are production-ready, observable, secure, scalable, and operationally sustainable.

Collaborates with Product, Engineering, Enterprise Data, Architecture, Security, Legal, and business stakeholders to translate opportunities into practical AI solutions.

Provides technical leadership and guidance on generative AI, large language models, agentic systems, retrieval-augmented generation, AI application architecture, evaluation, and emerging AI technologies.

Builds reusable engineering capabilities, patterns, components, and practices that accelerate delivery and enable teams to develop AI solutions consistently.

Champions responsible AI engineering practices, including appropriate evaluation, security, privacy, governance, risk management, and quality controls.

Uses engineering metrics, product outcomes, quality measures, and operational signals to identify opportunities, manage risk, and continuously improve team performance and solution value.

Leads technical discussions and decisions, clearly articulating tradeoffs, recommendations, risks, and implications to technical and non-technical stakeholders.

Evaluates emerging AI technologies, development frameworks, models, tools, and vendor capabilities and translates relevant advances into practical engineering opportunities.

Creates a culture of innovation, experimentation, learning, and continuous improvement while maintaining a strong focus on delivery and production quality.

Represents AI Engineering with senior stakeholders and cross-functional partners, building trust and alignment around technical direction and delivery.

Mentors engineers and technical leaders across the broader organization and contributes to the development of enterprise AI engineering practices.

Communicates engineering strategy, progress, risks, outcomes, and recommendations clearly to the Director and other organizational leaders.

Competencies:

Organizational Impact

Leads a professional engineering team responsible for executing strategic and operational priorities with measurable impact on organizational outcomes.

Plans and prioritizes work, allocates team capacity, and removes barriers to effective delivery.

Contributes to workforce planning, resource decisions, vendor considerations, and investment discussions for the AI Engineering function.

Problem Solving and Decision Making

Solves complex, ambiguous problems using strong technical judgment, structured analysis, experimentation, and practical engineering experience.

Makes sound decisions that balance business value, technical quality, security, risk, scalability, maintainability, and speed.

Anticipates technical and delivery risks and establishes pragmatic approaches to mitigate them.

Communication and Influence

Introduces and communicates complex AI and software engineering concepts clearly to technical and non-technical audiences.

Builds alignment across functions and influences decisions through credibility, clarity, evidence, and strong relationships.

Represents the AI Engineering team effectively with senior leaders, partners, and stakeholders.

Leadership and Talent Management

Manages and develops AI engineering professionals, creating an environment of high standards, accountability, collaboration, inclusion, and continuous learning.

Recruits, coaches, retains, and develops talent while establishing clear expectations and meaningful growth opportunities.

Conducts performance management and career development activities and participates in hiring, promotion, and compensation decisions.

Builds leadership capacity within the team by delegating effectively, developing emerging leaders, and creating opportunities for engineers to expand their impact.

Job Qualifications:

Minimum Qualifications:

Master’s degree in Computer Science, Software Engineering, Information Technology, Statistics, Artificial Intelligence, or a related technical field, or equivalent professional experience.

3+ years of progressively responsible professional experience in software engineering, AI engineering, machine learning, or a related technical discipline.

1+ years of experience leading or managing software, AI, or engineering professionals, including responsibility for delivery and team development.

Demonstrated experience designing and delivering production software or AI solutions in a modern cloud environment.

Strong software engineering and problem-solving skills, with experience in Python and/or other modern programming languages.

Practical experience with AI application development, generative AI, large language models, or related AI technologies.

Strong understanding of software engineering practices including architecture, testing, version control, CI/CD, observability, and production operations.

Ability to translate ambiguous business or technical problems into clear engineering approaches, priorities, and deliverables.

Strong communication skills and the ability to convey complex technical concepts and tradeoffs to technical and non-technical audiences.

Demonstrated ability to build strong cross-functional partnerships and lead complex initiatives through influence and collaboration.

Excellent critical thinking, planning, prioritization, organization, and execution skills.

Preferred Qualifications:

PhD. degree in Computer Science, Software Enginee