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POSTING INFORMATION
Internal Title Director, Chief AI Officer
Position Type Unclassified
Faculty / Non\-Faculty / Administration Administration
Pay Band
Level
Department IT Administration
Job Purpose
Reporting to the Chief Information Officer (CIO), the Director/CAIO will drive a coordinated, ethical, and strategic approach to the adoption and integration of artificial intelligence (AI) technologies throughout the university’s academic, administrative, legal, and research domains.
This position directly supports the university’s Quality Enhancement Plan (QEP) for Intentional AI, accelerates the modernization of legacy business processes, fosters innovation through AI Sandbox environments, and champions AI literacy and governance initiatives. The Director/CAIO will ensure that AI adoption aligns with the university’s mission, strategic objectives, and values, balancing technological innovation with ethical, transparent, and privacy\-focused practices.
Minimum Requirements
Advanced degree (Master’s required; Ph.D. preferred) in Computer Science, Information Systems, Data Science, or a related discipline. Demonstrated success in implementing AI solutions in academic and or enterprise environments. Deep understanding of ethical AI, data privacy, and higher education governance. Experience fostering interdisciplinary collaboration and supporting faculty research initiatives. Demonstrated experience in supporting workforce development initiatives, staff training programs and professional development programs, and outcomes evaluation. Candidates with an equivalent combination of experience and/or education are encouraged to apply.
Required Knowledge, Skills and Abilities
Thorough understanding of generative AI tools, predictive analytics, and natural language interfaces for institutional data. Ability to assess and automate high\-impact administrative workflows (such as admissions, advising, HR, procurement, and institutional reporting). Strong capability in establishing secure data protocols and managing AI Sandbox environments. Proven skills in vendor management, strategic technology partnerships, and fiscal resource stewardship. Expert knowledge in developing frameworks for data provenance, model interpretability, and digital trust deployment. Exceptional communication skills for campus\-wide stakeholder alignment, including managing resource hubs, technical briefing materials, and organizing technology and academic symposiums.
Additional Comments Regarding Position
Employee must be willing to work flexible hours including occasional nights and weekends to host or attend events and symposiums, and will be expected to travel to attend and present at regional and national AI/Tech conferences.
Special Instructions to Applicants
Please complete the application to include all current and previous work history and education. A resume will not be accepted nor reviewed to determine if an applicant has met the qualifications for the position.* Salary is commensurate with education/experience which exceeds the minimum requirements.
Offers of employment are contingent upon a successful background check.
All applications must be submitted online https://jobs.cofc.edu.
Salary - $150,000 \- $175,000
Posting Date 07/13/2026
Closing Date 07/27/2026
Benefits
- Insurance: Health/Dental/Vision
- Life Insurance
- Paid Leave: Sick/Annual/Parental
- Retirement
- Long Term Disability
- Paid Holidays
- Free CARTA Bus Service
- Employee Tuition Assistance Program (ETAP)
- Employee Assistance Program (EAP)
- Full Benefits Package – Click Here
Open Until Filled No
Posting Number 2026109
EEO Statement
The College of Charleston is an equal opportunity employer and does not discriminate against any individual or group on the basis of sex, gender (including gender identity and/or expression), pregnancy, race, religion, color, national origin, age, disability, military or veteran status, sexual orientation, genetic information, and other classifications protected by applicable federal, state, and local laws. For more information, please visit eop.cofc.edu.
Job Duties
Activity
Operates with high executive autonomy to develop, drive, and oversee a comprehensive, multi\-year AI Roadmap aligned with institutional priorities and the Quality Enhancement Plan (QEP) for Intentional AI. The CAIO establishes and chairs the University AI Governance Framework and Council, formulating university\-wide policies through a shared governance model across academic, administrative, and research stakeholders. Additionally, the role provides matrixed leadership to ensure all AI deployment — including risk management for data provenance and model interpretability — adheres to university, state, and federal guidelines while balancing rapid innovation with ethical, privacy\-focused practices.
Essential or Marginal Essential
Percent of Time 30
Activity
Collaborate closely with Academic Affairs, CETL, and the QEP Director as the institution’s AI Subject Matter Expert (SME), leveraging deep technical expertise and advanced platform knowledge to technically integrate AI literacy outcomes into the curriculum, support faculty and staff instructional design, and build comprehensive frameworks to support student innovation.
Essential or Marginal Essential
Percent of Time 25
Activity
Partner with institutional units (Admissions, Advising, HR, Procurement, Financial Affairs) to assess, design, and deploy high\-impact administrative process automation pilots and predictive analytics dashboards to reduce manual workload.
Essential or Marginal Essential
Percent of Time 20
Activity
Design, launch, and oversee secure, enterprise\-wide AI Sandbox environments. Lead the design and oversight of AI data infrastructure, secure API integrations, and accessibility compliance measures.
Essential or Marginal Essential
Percent of Time 10
Activity
Create and manage strategic relationships with AI solution providers to maximize fiscal responsibility and avoid multi\-departmental tool duplication. Serve as primary liaison for national and consortial AI initiatives.
Essential or Marginal Essential
Percent of Time 10
Activity
Manage the campus\-wide AI resource hub, continuous staff training and development series, monthly newsletters, and organize university Technology and Higher Education Symposiums and compile an annual AI Innovation and Impact Report.
Essential or Marginal Essential
Percent of Time 5
Salary Context
This $150K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At COLLEGE OF CHARLESTON, 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 $218,750 based on 3,817 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $250,000. This role's midpoint ($162K) sits 26% below the category median. Disclosed range: $150K to $175K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
COLLEGE OF CHARLESTON AI Hiring
COLLEGE OF CHARLESTON has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Charleston, SC, US. Compensation range: $175K - $175K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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