Edtech.com's Summary

WGU Corporation is hiring a Manager, AI Operations and Enablement. This role provides technical leadership for the AI Operations and Enablement team, managing MLOps Engineers, AI Engineers, and Architects to build and scale the enterprise AI/ML platform, foster AI adoption, and guide development, deployment, and governance of machine learning and generative AI applications. The Manager partners with business units to identify impactful AI use cases, accelerate timelines, and ensure responsible AI practices while focusing on end-user experience and business impact.

Highlights

Lead and manage the AI Operations and Enablement team, including hiring, coaching, and talent development.
Oversee enterprise AI/ML platform operations such as model serving infrastructure, feature stores, vector databases, and evaluation frameworks.
Design and implement MLOps and LLMOps processes, integrating them with end-user applications.
Manage deployment of machine learning and generative AI models, develop pipelines, roadmaps, and enablement programs.
Evaluate and integrate foundation and embedding models; manage prompt engineering and agent orchestration patterns.
Develop and enforce AI governance standards, including model monitoring, drift detection, bias auditing, and compliance reporting.
Drive AI enablement by identifying automation opportunities and collaborating with business units to deploy use cases.
Require expertise in Agile methods (SCRUM, test-driven development), Python coding, ML model management platforms (MLflow, SageMaker), and GenAI technologies.
Qualifications include a B.S. and M.S. in related fields, 7+ years in software engineering or ML, 5+ years in AI/ML contexts, and leadership experience in AI-focused teams.
Compensation range is $170,400 to $281,200; full-time salaried position with extensive benefits including bonuses, health coverage, paid leave, and tuition discounts.

Manager, AI Operations and Enablement Full Description

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 715Pay 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 various audiences, including senior leadership.
Perform other related duties as assigned.

Knowledge, Skills, and Abilities

Strong people and management skills for interaction with staff, colleagues, cross-functional teams, and third parties.
Ability to translate complex technical requirements into functional ML and GenAI solutions using industry best practices.
Expertise in Agile methods, including SCRUM and test-driven development.
Excellent verbal and written communication skills; capable of working in self-managed or Agile/Scrum teams.
Proven experience as an ML/AI/AIOps Engineer with a history of building AI/ML architecture and pipelines.
Strong understanding of GenAI concepts, including retrieval-augmented generation architectures, agent frameworks, prompt engineering, fine-tuning, and evaluation methodologies.
Ability to assess and communicate AI feasibility, ROI, and risk to technical and non-technical stakeholders.

Competencies

Organizational Impact

Manage a team focused on executing operational and strategic plans, contributing measurably to departmental and organizational results.
Assign and distribute work effectively.
Compile data for preparing budgets, including AI/ML infrastructure and API consumption costs.
Drive measurable AI adoption metrics throughout the organization.

Problem Solving and Decision Making

Improve processes or systems to enhance job area performance.
Handle broad, undefined assignments requiring analytical concepts, investigation, and prior knowledge.
Evaluate build vs. buy decisions for AI tooling and infrastructure.

Communication and Influence

Communicate regularly within the job area and with external groups, including business stakeholders, product teams, and senior leadership.
Ensure compliance with University policies and may influence others to justify and gain cooperation for AI governance policies.
Act as an AI evangelist, educating teams on platform capabilities and responsible AI usage.

Leadership and Talent Management

Supervise a team of professionals, ensuring proper training, and participate in hiring, firing, promotion, and performance reviews.
May handle technical assignments alongside supervisory duties.
Foster a culture of experimentation, continuous learning, and knowledge sharing within the team.

Job Qualifications

B.S. and M.S. degree in Computer Science, Software Engineering, Data Science, Machine Learning, Math, Physics, or related field.
7+ years of experience in Software Engineering, Data Science, or Machine Learning.
5+ years of experience working in an AI/ML context alongside Data Scientists or ML Engineers.
5+ years of experience building MLOps pipelines and processes (CI/CD and CT) in cloud architecture (preferably Databricks).
2+ years of hands-on experience with GenAI technologies including LLMs, RAG pipelines, agent frameworks, or fine-tuning workflows.
Ability to understand and articulate trade-offs for various approaches to machine learning and AI platform solutions.
Fluency in Python and history of writing clean, clear code as part of a team.
Experience with ML model management platforms, such as MLflow or SageMaker.
Experience with production model validation and monitoring techniques, including drift detection and GenAI evaluation (e.g., MLflow scorers, RAGAS, DeepEval).
Significant hands-on experience with large datasets.
3+ years of experience leading a Data Science, ML, or AI-focused team.
2+ years of experience managing projects end-to-end.
5+ years of experience collaborating with business and other teams.
Business acumen and understanding.
Experience with AI governance, responsible AI principles, and cost management for AI workloads.

Preferred Qualifications

Experience with vector databases and embedding pipelines.
Experience with LLM serving infrastructure and optimization (quantization, caching, batching).
Experience with AWS Bedrock, Databricks Foundation Model APIs, or similar managed AI services.
Experience building internal AI enablement programs or centers of excellence.
Familiarity with AI safety frameworks and regulatory considerations.

Position & Application Details
Full-Time Regular Positions (classified as regular and working 40 standard weekly hours): This is a full-time, regular position (classified for 40 standard weekly hours) that is eligible for bonuses; medical, dental, vision, telehealth and mental healthcare; health savings account and flexible spending account; basic and voluntary life insurance; disability coverage; accident, critical illness and hospital indemnity supplemental coverages; legal and identity theft coverage; retirement savings plan; wellbeing program; discounted WGU tuition; and flexible paid time off for rest and relaxation with no need for accrual, flexible paid sick time with no need for accrual, 11 paid holidays, and other paid leaves, including up to 12 weeks of parental leave.

How to Apply: If interested, an application will need to be submitted online. Internal WGU employees will need to apply through the internal job board in Workday.

Additional Information

Disclaimer: The job posting highlights the most critical responsibilities and requirements of the job. It's not all-inclusive.

Accommodations: Applicants with disabilities who require assistance or accommodation during the application or interview process should contact our Talent Acquisition team at recruiting@wgu.edu.

Equal Employment Opportunity: All qualified applicants will receive consideration for employment without regard to any protected characteristic as required by law.

Original Job Description