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About the Opportunity
The AI Enablement Partner serves as an internal consultant and strategic partner to University of North Carolina System Office (UNCSO) divisions, helping teams identify opportunities to improve business processes, enhance services, and increase operational effectiveness through the responsible use of artificial intelligence.
This position will be employed and paid through the School of Government Services (SOG Services), an associated entity of the University of North Carolina at Chapel Hill. This position is a UNC System Office position that will provide dedicated support to UNC System Office divisions.
Working collaboratively across Finance, Human Resources, Academic Affairs, Legal, Communications, Information Technology, and other System Office units, this position facilitates the evaluation, design, implementation, and adoption of AI-enabled solutions that support institutional priorities while ensuring alignment with governance, security, privacy, and ethical standards. The position combines business analysis, project coordination, process improvement, change management, and emerging AI techno elogies to help internal teams solve business challenges and improve organizational effectiveness.
This position may be eligible for a hybrid or remote work arrangement, consistent with System Office policy. UNC System employees are generally required to reside in North Carolina, within a 2-hour commuting distance of their assigned duty station.
About the UNC System Office
The UNC System Office includes the offices of the President and other senior administrators of the multi-campus University of North Carolina System. The UNC System is a treasured public institution dedicated to serving the people of North Carolina through world-class teaching, research, and community engagement. Today, nearly 250,000 students are enrolled in our 16 universities across the state and at the NC School of Science and Mathematics. System Office staff is responsible for executing the policies of the UNC Board of Governors and providing University-wide leadership in the areas of academic affairs, business and financial management, long-range planning, student affairs, research, legal affairs, and government relations. The UNC System Office also has administrative oversight of a number of University affiliates, including PBS North Carolina, the North Carolina Arboretum, the NC State Education Assistance Authority, and University of North Carolina Press.
About the Team
The Office of the Chief Operating Officer (COO), reporting directly to the President of the University, is responsible for overseeing internal and campus-focused operational priorities, including leadership and coordination of key teams such as Human Resources, IT, Enterprise Data, Project Management, Advancement Shared Services, HR Shared Services, Workforce Policy, and Operational Strategy. These divisions support both the UNC system office's internal operations and provide strategic leadership across campus. The COO's office advises the President, fosters relationships on his behalf, and engages with Chancellors to further the President's strategic priorities, ensuring alignment between operational practices and institutional goals while driving efficiency and innovation throughout the University.
Minimum Qualifications (Knowledge, Skills, and Abilities)
Bachelor's degree in Business Administration, Information Technology, Computer Science, Public Administration, or a related field and two years of progressively responsible experience in business analysis, project management, process improvement, information technology, organizational consulting, or related work; or an equivalent combination of education and experience. Knowledge of business process analysis and continuous improvement methodologies. Ability to build collaborative relationships with diverse internal stakeholders. Strong facilitation and communication skills. Ability to translate technical concepts into language appropriate for non-technical audiences. Knowledge of project management principles and change management practices. Ability to manage multiple initiatives simultaneously. Strong analytical and problem-solving skills. Ability to learn emerging AI technologies quickly and apply them to business challenges. Experience working collaboratively across organizational boundaries.
Preferred Qualifications
Experience supporting enterprise technology or digital transformation initiatives. Experience implementing or supporting AI-enabled business solutions. Familiarity with Microsoft Copilot, ChatGPT Enterprise, Microsoft Power Platform, or similar AI platforms. Experience facilitating cross-functional projects. Knowledge of process improvement methodologies (Lean, Six Sigma, Business Process Management). Experience delivering training or change management activities. Experience working within higher education or a large public-sector organization.
How to Apply
To apply for this position please submit your application with SOG Services using this link.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At University of North Carolina, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
University of North Carolina AI Hiring
University of North Carolina has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Raleigh, NC, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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