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About This Role
University of St. Thomas Vice President for Academic Affairs
The Vice President for Academic Affairs (VPAA) is the Chief Academic Officer of the University, reporting directly to the President. The VPAA's primary responsibilities focus on stewardship of the University's academic mission in Catholic higher education, strategic development of academic programs, mission-focused faculty development, oversight of program quality/effectiveness and achievement of UST strategic goals for its academic programs. The VPAA serves as a key member of the University's leadership team.
Stewardship and Strategic Role Responsibilities:
- Assure that all academic programs are aligned with the University's mission and grounded in the tradition of the Basilian Fathers and that they are effectively communicated to all constituents, integrated into the work of every program, and regularly reviewed and re-affirmed as needed.
- Support the UST President's strategic and operational priorities, integrating these into the University's academic mission and planning processes.
- Guide strategic development, implementation, and evaluation of UST's academic programs in alignment with the University's strategic goals.
- Facilitate the conditions and relationships that support creativity, innovation, formation, and development of faculty, staff, and students.
- Maintain and develop the internal and external partnerships that support the achievement of the University's academic mission.
Support/Oversight Responsibilities – Deans and Faculty:
- Facilitate the strategic and operational effectiveness of the deans, department chairs, and faculty in alignment with the Catholic intellectual tradition.
- hair the Deans' Council and facilitate its role as a leadership body responsible for stewarding the mission, strategic development of academic programs, support for innovation within and across schools and programs, and the growth and sustainability of initiatives.
- Collaborate with the Faculty Senate leadership, Advisory Council, and established governance structures to ensure a faculty voice and effective shared governance decision-making and accountability.
- Oversee processes associated with dean/department chair/director status, including recruitment, appointment, contracts orientation, performance evaluation, grievance/complaints, promotion, and tenure.
- Oversee processes associated with faculty status, including recruitment, appointment, contracts orientation, performance evaluation, grievance/complaints, promotion, tenure sabbaticals, leave of absence, and designation of emeritus status.
- Facilitate the development and implementation of plans and resource distribution for faculty engagement in scholarship fostering, in particular, scholarship and dialog that address contemporary issues in light of Church teaching, the harmony of faith and reason, and the dignity of the human person.
- Create, implement, and evaluate processes and procedures essential to an effective and collaborative institution of Catholic higher education.
Support/Oversight Responsibilities – Academic Programs:
- Support deans and faculty in monitoring and improving academic program quality and student learning outcomes.
- Collaborate with deans, faculty, staff, and colleagues in Finance, Enrollment Management, Student Affairs, Institutional Advancement, Marketing, and Human Resources to develop and support programs that contribute to sustainable growth and high-quality learning outcomes.
- Oversee the development and orientation of deans, department chairs, and faculty in light of the Basilian heritage and the Church's teaching on higher education. Facilitate growth and enhancement of academic programs through support for sponsored research, grants, and partnerships.
- Serve as Dean of Graduate Studies.
Support for UST President and Board of Directors:
- Provide leadership at the University level as a member of the President's Council and as liaison to the Academic Affairs Committee of the Board of Directors and other Board committees as directed.
- Assure that academic program reports and proposals presented to the President and Board of Directors meet appropriate quality standards to support effective review, decision-making, and implementation of Board action.
Administrative and Regulatory Responsibilities:
- Facilitate the work of deans, administrators, department chairs, faculty, and staff in assessing and continuously improving infrastructure, resources, and operations that support academic programs.
- Oversee resource acquisition and budget management in a manner consistent with Catholic social teaching.
- Assure compliance with regulatory requirements and reporting associated with academic programs for SACSCOC and specialty accreditations.
- Assure that academic and faculty policies relevant to the educational mission are developed in congruence with university and regulatory requirements, reviewed and updated regularly, and distributed and communicated clearly to faculty, staff, students, and the University community.
Education:
- Doctoral degree, preferably a PhD in a liberal arts and sciences or professional discipline or STD (Doctorate in Sacred Theology) from an accredited institution.
Experience and Essential Attributes:
- 6-7 years of progressively increasing responsibility as a senior academic leader at the VP or dean level including demonstrated experience in accreditation, program review, curriculum design, and assessment; and a proven record of positive creative results and accomplishments.
- Must be an active member in good standing in the Catholic Church and demonstrate a comprehensive understanding of *Ex Corde Ecclesiae.*
- Familiarity with the contributions of the Catholic intellectual tradition to higher education and the Church's commitment to the harmony of faith and reason.
- Proven ability to inspire, collaborate and negotiate with diverse constituencies, even in resource-constrained circumstances. The individual must be able to lead and inspire the faculty to new levels of engagement to champion innovation and embrace a growth mindset while fostering the University's Catholic mission.
- Experienced leader with a clear, focused commitment to teaching, learning and academic excellence to promote student success.
- Seasoned professional with experience in governance who fosters unity and a sense of working together for the good of the University.
- Demonstrated knowledge, skills, and abilities in academic administration including regulations, laws, and other general related policies.
- Proven experience of fostering excellence in teaching and learning through the use of innovative and effective technology.
- Demonstrated record of fiscal responsibility and accountability.
- Demonstrated ability to attract, recruit, onboard and develop a strong faculty.
- Recognized as having an academic vision with attention to the future of higher education and emerging trends.
- Be highly ethical, trustworthy, credible and respectful of diverse views and opinions and as someone who is known for building consensus and being a good listener.
- Possess highly effective communication skills.
*The University of St. Thomas is committed to the religious, ethical, and intellectual traditions of Catholic higher education. As permitted by law, practicing Catholics who will advance the mission of the school are preferred for this position. However, the University invites all qualified applicants to apply.*
The University of St. Thomas is an equal opportunity employer.
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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At University of St. Thomas Houston, 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 Required
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 $154,000 based on 8,743 positions with disclosed compensation.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
University of St. Thomas Houston AI Hiring
University of St. Thomas Houston has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Houston, TX, US.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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