Manager, AI Operations and Enablement

Western Governors University•Raleigh, NC

15h

About The Position

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.

Requirements

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.
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.

Nice To Haves

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.

Responsibilities

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.
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.
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.
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.
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.