Interested in this AI/ML Engineer role at Virginia Union University?
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Virginia Union University (VUU) is a distinguished private institution founded in 1865 that proudly upholds a legacy of academic excellence and community engagement. With a historic 86-acre campus, it offers a dynamic and nurturing environment for its 1,568 undergraduate and graduate students. Virginia Union University is recognized in the 2024 edition of U.S. News & World Report, holding a prestigious position as #39 among Historically Black Colleges and Universities.
Five fundamental principles (core values) guide Virginia Union University. Each principle is essential to our ability to provide a Best-In-Class educational experience for students, faculty, and staff. These principles help our community understand the right path to fulfilling our institution's mission. The principles are Innovation, Spiritual Formation, Integrity, Diversity & Inclusion, and Excellence.
The Chief AI Development Officer (CAIDO) is Virginia Union University's senior enterprise leader for artificial intelligence. Reporting to the Senior Vice President for Infrastructure Management & Planning / Chief of Staff, with strategic access to the President, the CAIDO establishes the University's AI strategy, governance framework, policies, training protocols, development standards, and implementation roadmap. The role ensures that AI is adopted responsibly, securely, equitably, and in direct support of VUU's mission, strategic priorities, student success, academic excellence, operational effectiveness, and institutional growth. The CAIDO is both strategist and builder. The position leads the development of AI-enabled solutions and helps divisions design, build, test, deploy, and govern AI agents that improve business processes across the University. Working across academic and administrative units, the CAIDO creates a common institutional approach to AI while enabling departments to develop practical solutions within approved standards and controls. As a senior leader, the CAIDO will help position VUU as a national model for responsible, applied AI in higher education - preparing students and employees for an AI-shaped future while using technology to expand access, capability, and institutional impact.Reporting Relationship & Scope
Division
Infrastructure Management & Planning (IMP) Reports to
Senior Vice President for IMP / Chief of Staff Strategic access
President; Cabinet and Board briefings as requested Scope
University-wide AI strategy, policy, training, governance, development, adoption, and agent enablement Key partners
Academic Affairs, IT, Institutional Effectiveness, Student Affairs, Enrollment, Finance, Advancement, HR, VUU Global, and other University units
Essential Duties and Responsibilities
1. Enterprise AI Strategy, Policy & Governance
- Develop, publish, and maintain a multi-year University AI Strategy and implementation roadmap aligned with VUU strategic and institutional-effectiveness priorities.
- Establish and maintain University-wide AI policies, standards, acceptable-use requirements, procurement criteria, data protections, model-risk controls, and responsible-AI practices.
- Chair or lead the University AI Governance Council and establish review, approval, escalation, and exception processes for institutional AI use.
- Advise the President, Cabinet, and Board on AI opportunities, investments, emerging risks, regulatory developments, and institutional readiness.
- Create standards for security, privacy, accessibility, bias/fairness, transparency, human oversight, records management, and lifecycle governance for AI systems and agents.
2. University-Wide AI Training & Adoption
- Establish tiered AI training protocols for executives, faculty, staff, students, technical teams, and other role-based audiences, including required foundational training where appropriate.
- Develop an institutional AI literacy program covering responsible use, prompt and workflow design, data handling, verification, academic integrity, agent use, and human accountability.
- Create training standards, playbooks, templates, office hours, workshops, and communities of practice that enable divisions to adopt AI consistently and safely.
- Partner with Academic Affairs to support faculty development, curricular AI literacy, credentials, and student preparation for an AI-enabled workforce.
- Measure training completion, adoption, proficiency, risk awareness, and demonstrated business or academic impact.
3. AI Agent Development & Business Transformation
- Lead and enable the design, development, testing, deployment, and continuous improvement of AI assistants, agents, automations, retrieval-augmented systems, predictive tools, and other AI solutions.
- Help business units identify high-value processes and build agents that reduce cycle time, improve service, strengthen decision-making, increase accuracy, and automate appropriate repetitive work.
- Establish a reusable enterprise agent-development framework, including approved architectures, templates, identity/access controls, data connectors, testing protocols, human-in-the-loop controls, monitoring, documentation, and retirement standards.
- Create an intake and prioritization process for AI use cases based on strategic value, feasibility, risk, cost, measurable benefit, and readiness.
- Partner with IT to integrate AI securely with approved enterprise platforms, data sources, ERP, LMS, CRM, collaboration tools, and other University systems.
- Use Lean Six Sigma / process-improvement methods to redesign workflows before or alongside automation, ensuring AI improves the underlying business process rather than simply digitizing inefficiency.
4. Data, Technology & Responsible AI Architecture
- Partner with IT and Institutional Effectiveness to strengthen AI-ready data architecture, data quality, metadata, access controls, integration, analytics, and governance.
- Establish technical standards for model selection, evaluation, testing, observability, performance, security, versioning, auditability, and lifecycle management.
- Evaluate AI vendors, platforms, enterprise licenses, and emerging technologies in coordination with Procurement, Finance, IT, Legal/Compliance, and other stakeholders.
- Maintain an institutional inventory of approved AI systems, agents, use cases, owners, data dependencies, risk classifications, and performance outcomes.
5. Institutional Impact, Partnerships & Leadership
- Deliver AI-enabled solutions across administrative and academic operations, including student success, enrollment, finance, advancement, HR, facilities, communications, VUU Global, and other priority areas.
- Define and report enterprise AI performance measures, including adoption, productivity, service improvement, cost avoidance, revenue impact, student outcomes, risk, and return on investment.
- Cultivate partnerships with technology companies, HBCU consortia, government agencies, foundations, and research organizations; pursue grants and sponsored funding in coordination with appropriate University offices.
- Recruit, develop, and lead AI developers, data/automation professionals, student interns, contractors, and cross-functional project teams as the function grows.
- Represent VUU externally as a leader in responsible AI adoption, HBCU innovation, workforce readiness, and applied institutional transformation.
Qualifications
- Master's degree in computer science, data science, artificial intelligence, information systems, engineering, business/technology management, or a related field.
- Progressive leadership experience in AI, software, data, automation, digital transformation, or enterprise technology, including experience leading complex cross-functional initiatives.
- Demonstrated ability to take AI solutions from concept through production and to translate emerging technology into practical institutional or business outcomes.
- Working knowledge of large language models, agentic systems, retrieval-augmented generation, workflow automation, cloud AI platforms, APIs/integrations, data governance, and AI operations.
- Experience developing or implementing technology governance, policy, security/privacy controls, training, change management, and responsible-AI practices.
- Executive-level communication skills and the ability to explain technical opportunities, risks, and decisions to trustees, executives, faculty, staff, students, and external partners.
Preferred
Masters degree required; strong AI knowledge, higher-education experience a plus; familiarity with ERP/LMS/CRM/student systems; grant or sponsored-funding experience; SACSCOC/institutional-effectiveness knowledge; Lean Six Sigma or comparable process-improvement credential.Core Competencies
Enterprise AI strategy and execution • AI governance and responsible innovation • Training and organizational capability building • Agentic AI and automation • Business-process redesign • Data and technology architecture • Change leadership • Executive communication • Cross-divisional collaboration • Equity, access, and HBCU mission alignmentWork Environment
The position is based primarily on the Richmond campus and requires regular engagement across University divisions, with occasional travel to conferences, partner sites, and professional events. Evening or weekend availability may be required for executive/Board activities, major training events, or critical technology deployments. Other duties may be assigned by the Senior Vice President for IMP / Chief of Staff or the President.
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 Virginia Union University, 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. C-Level-level AI roles across all categories have a median of $250,000.
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.
Virginia Union University AI Hiring
Virginia Union University has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Richmond, VA, 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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