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About This Role
Required Application Materials
A single PDF or Word document containing a resume, letter of interest, and a list of the names and contact information for three (3\) professional references is required to apply.
Important Instructions:
When prompted to upload your “resume”, upload your combined documents as one file.
- Please do not upload documents to “Cover Letter” or “References”.
If the required application materials were not uploaded at the “resume” prompt, please withdraw your application, and re\-apply to upload your combined documents as one file.
Job Summary
The AI/Automation Analyst is an entry\-level professional responsible for developing, implementing, and maintaining automation solutions that improve operational efficiency, data quality, and decision\-making across the university. This position supports the adoption of artificial intelligence, robotic process automation (RPA), workflow automation, and low\-code/no\-code technologies to streamline administrative, academic, and compliance\-related processes.
Working under the guidance of senior analysts, engineers, and institutional leaders, the position will assist in identifying automation opportunities, developing AI\-enabled solutions, integrating enterprise systems, and supporting data\-driven initiatives.
Major Responsibilities/Duties/Critical Tasks
Automation Development
- Design, develop, test, and maintain workflow automation solutions using Microsoft Power Platform, Copilot Studio, Power Automate, and related tools.
- Assist in developing robotic process automation (RPA) solutions to reduce manual work and improve operational efficiency.
- Create automated data collection, validation, and reporting processes.
- Support integration of data between institutional systems, cloud platforms, and third\-party applications.
Artificial Intelligence Solutions
- Assist with the deployment and support of generative AI and machine learning solutions.
- Develop AI\-powered tools, chatbots, virtual assistants, and intelligent agents for administrative and student support functions.
- Support prompt engineering, knowledge base development, and retrieval\-augmented generation (RAG) implementations.
- Evaluate emerging AI technologies and recommend opportunities for institutional use.
Data and Analytics Support
- Collaborate with Institutional Research, Information Technology, Enrollment Management, Academic Affairs, and other operational units.
- Assist in creating automated dashboards, reports, and performance monitoring tools.
- Support data preparation, transformation, and quality assurance activities.
- Contribute to data governance, documentation, and process standardization efforts.
Business Process Improvement
- Participate in process improvement projects focused on efficiency, accuracy, and scalability.
- Document business requirements and workflow processes.
- Identify repetitive manual tasks suitable for automation.
- Assist stakeholders in implementing and adopting automation solutions.
Operational Support
- Monitor production automations and troubleshoot issues.
- Maintain technical documentation, process diagrams, and user guides.
- Participate in testing, deployment, and user training activities.
- Provide general support for enterprise automation platforms and AI initiatives.
Required Education/Experience
- Bachelor's degree in Computer Science, Data Analytics, Data Science, Business Analytics, Information Systems, Statistics, Mathematics, Economics, Institutional Research, or a closely related field.
- Two (2\) years of related experience.
Preferred Education/Experience
- Master’s degree in a closely related field.
- One (1\) year of experience in higher education data analytics, business intelligence, institutional research, or related analytical work.
- Relevant experience in software development, automation, analytics, data management, or related technical work.
Accompanying Knowledge, Skills, Abilities and Competencies
- Basic programming proficiency in Python, SQL, or other programming language.
- Ability to translate business requirements into technical solutions.
- Understanding of process automation methodologies.
- Ability to troubleshoot automation workflows and data issues.
- Knowledge of responsible AI principles, data privacy, and security practices.
- Ability to explain technical concepts to non\-technical audiences.
- Demonstrated curiosity and commitment to continuous learning.
- Ability to maintain the security or integrity of critical infrastructure, which may include communications systems, computer networks and systems, cybersecurity systems, electrical grid, hazardous waste treatment or water treatment system.
About The University of Texas at Tyler
The University of Texas at Tyler is part of the prestigious University of Texas System that includes 14 institutions located throughout the state. Founded in 1971, UT Tyler today enrolls 10,000 students and consists of six colleges.
Designated an R2 research institution by the Carnegie Classification of Institutions of Higher Education, UT Tyler supports our surrounding region through innovation intended to uplift communities, spearhead sustainability initiatives, preserve ecosystems and support entrepreneurship. UT Tyler has the highest Carnegie classification in East Texas.
Our beautiful Tyler campus features more than 200 acres nestled along a lake and surrounded by thick pine and oak forests, providing a picturesque location for study and recreation. We also offer instructional sites at The University of Texas Health Science Center at Tyler and in Palestine, Longview, and Houston.
With more than 80 bachelor's, master's and doctoral degree programs offered, UT Tyler provides a wealth of learning opportunities and dynamic programs.
For more information, please visit https://www.uttyler.edu/about/.
Why work for UT Tyler? Find out more: Why Work for UT Tyler?
Additional Information
The retirement plan for this position is Teacher Retirement System of Texas (TRS), subject to the position being at least 20 hours per week and at least 135 days in length.
This position is security\-sensitive and subject to Texas Education Code Section 51\.215, which authorizes the employer to obtain criminal history record information. Applicants selected must be able to show proof of eligibility to work in the United States by time of hire.
For more information regarding benefits offered by UT Tyler, please visit https://www.uttyler.edu/offices/human\-resources/employees/benefits.
EEO Statement
The University of Texas at Tyler is an Equal Employment 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At The University of Texas at Tyler, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
The University of Texas at Tyler AI Hiring
The University of Texas at Tyler has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Tyler, TX, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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