Manager, AI Operations and Enablement

If you’re passionate about building a better future for individuals, communities, and our country—and you’re committed to working hard to play your part in building that future—consider WGU as the next step in your career.

Driven by a mission to expand access to higher education through online, competency-based degree programs, WGU is also committed to being a great place to work for a diverse workforce of student-focused professionals. The university has pioneered a new way to learn in the 21st century, one that has received praise from academic, industry, government, and media leaders. Whatever your role, working for WGU gives you a part to play in helping students graduate, creating a better tomorrow for themselves and their families.

The salary range for this position takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs.

At WGU, it is not typical for an individual to be hired at or near the top of the range for their position, and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is:
 

Grade: Management Technical 715

Pay Range: $170,400.00 - $281,200.00

Job Description

The Manager, AI Operations and Enablement provides technical leadership and vision for the AI Operations and Enablement team. This individual oversees a group of MLOps Engineers, AI Engineers, and Architects responsible for building and scaling WGU’s enterprise AI/ML platform, as well as fostering AI adoption throughout the organization. The role requires a service provider mindset, utilizes Agile practices, and includes technical guidance and oversight for the development, deployment, and governance of both traditional machine learning models and generative AI applications (such as retrieval-augmented generation, agents, and fine-tuned large language models). The Manager partners with business units to identify high-impact AI use cases, accelerate production timelines, and establish responsible AI practices. This position involves personnel selection, development, and evaluation to ensure efficient operations, with a strong focus on end-user experience and business impact.

This position offers the opportunity to directly influence the experience of over 10,000 staff and 180,000 students by enabling AI-driven solutions that enhance student outcomes, streamline operations, and scale personalized learning. The Manager collaborates closely with Data Science, Analytics, Product, and Engineering teams.

 

Essential Functions and Responsibilities

Team & Operations Management

• Manage the AI Operations and Enablement team to consistently deliver quality solutions on time and within budget and scope. Oversee hiring, coaching, and talent development.

• Supervise function operations, including employees, vendor resources, and business support staff.

Platform & Infrastructure

• Oversee enterprise AI/ML platform operations, including model serving infrastructure, feature stores, vector databases, and evaluation frameworks.

• Lead design, implementation, and execution of MLOps and LLMOps processes, integrating with end-user applications.

• Manage ML and GenAI model deployment as a product, including developing pipelines, roadmaps, and enablement programs.

GenAI & Emerging Technologies

• Lead the evaluation, selection, and integration of foundation models, embedding models, and AI services (e.g., Databricks Foundation Model APIs, AWS Bedrock).

• Manage prompt engineering standards, retrieval-augmented generation pipeline architecture, and agent orchestration patterns.

• Stay current with emerging AI/ML technologies and translate new capabilities into actionable platform improvements.

Governance & Standards

• Develop and enforce standards and guidelines for ML and GenAI development, deployment, and governance to ensure compliance with responsible AI policies.

• Establish and maintain AI governance frameworks, including model monitoring, drift detection, bias auditing, cost tracking, and compliance reporting.

Enablement & Collaboration

• Drive AI enablement by identifying automation opportunities, conducting feasibility assessments, and partnering with business units to move use cases from ideation to production.

• Build and foster relationships with other teams and manage expectations.

• Present and communicate results, status, and AI strategy to va