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About This Role
Title: Associate AI Platform Engineer
Employee Classification: Associate AI Platform Engineer
Campus: University of North Texas \- System Administration
Division: SYS\-Information Technology
SubDivision\-Department: SYS\-Information Technology
Department: SYS\-Information Technology\-925000
Job Location: Denton
Salary: Commensurate with experience
FTE: 1\.000000
Retirement Eligibility:
About Us \- Values Overview
Welcome to the University of North Texas System. The UNT System includes the University of North Texas in Denton and Frisco, the University of North Texas at Dallas and UNT Dallas College of Law, and University of North Texas Health Fort Worth. We are the only university system based exclusively in the robust Dallas\-Fort Worth region. We are growing with the North Texas region, employing more than 14,000 employees, educating a record 49,000\+ students across our system, and awarding nearly 12,000 degrees each year.
We are one team comprised of individuals who are committed to excellence, curiosity and innovation. We are transforming lives and creating economic opportunity through education. We champion a people\-first values\-based culture where We Care about each other and those we serve. We believe that we are Better Together because we foster an environment of respect, belonging, and access for all. We demonstrate Courageous Integrity through setting exceptional standards and acting in the best interest of our communities. We are encouraged to Be Curious about opportunities for learning, creating, discovering, and innovating, and are encouraged to learn from failure. Show Your Fire by joining our team and exhibiting your passion and pride in your work as part of our UNT System team.
Learn more about the UNT System and how we live our values at www.UNTSystem.edu.
Department Summary
UNT System Information Technology is a component of The University of North Texas System. The mission of UNT System Information Technology is simple: to be the best at delivering customer\-aligned IT solutions and services. Our services are provided to all member institutions using an IT shared services governance model.
UNT System Information Technology provides the primary technology used by all campuses to support their operations. These include: shared resources of data centers, computing hardware, software applications, network and voice communications, collaboration and productivity capabilities, central web services, risk management, and security and compliance services. As a department, we strive to provide the most professional personnel possible to serve our campus constituents.
The University of North Texas is a state employer offering great benefits, including medical, dental, life, retirement, ample paid time off, and great work/life balance.
Position Overview
The Associate AI Platform Engineer is responsible for building AI agents on multiple AI platforms, assist in training others on the use of those platforms, manage projects and testing efforts. They will be instrumental in governance activities and the expansion of AI use throughout the enterprise.
Minimum Qualifications
Bachelor's degree in Computer Science, Information Systems, Computer Engineering, or a related field; or any equivalent combination of education, experience, or training.
Knowledge, Skills and Abilities
- Foundational knowledge of AI platform architecture, deployment, and operations.
- Familiarity with or eagerness to learn Model Context Protocol (MCP), agent orchestration, and Microsoft Azure AI services.
- Proficiency or coursework in programming languages such as Python, C\#, or JavaScript relevant to AI agent development.
- Understanding of cloud infrastructure concepts, including networking and authentication basics.
- Ability to assist with monitoring, logging, and operational controls for enterprise platforms.
- Skill in producing documentation, templates, and reference materials.
- Ability to translate business requirements into technical solutions with guidance.
- Strong collaboration and communication skills with cross\-functional teams.
- Awareness of AI governance, regulatory compliance, and data privacy principles.
- Analytical aptitude and a structured approach to problem\-solving.
- Eagerness to learn and adapt to rapidly evolving AI technologies and platforms.
Preferred Qualifications
- Coursework, academic projects, or hands\-on experience with cloud platforms, AI tools, or agent frameworks.
- Familiarity with Azure AI Foundry, Azure OpenAI, or similar cloud AI services.
- Exposure to MCP servers or agent orchestration concepts.
- Experience with Cloudforce’s nebulaONE platform.
- Interest in or exposure to higher education or regulated environments.
Required License/Registration/Certifications
Job Duties
- Design and build AI agents.
- Design and deploy secure back\-end infrastructure and tools to assist in the integration and build of agents.
- Develop multi\-model integrations and manage secure access to AI.
- Develop and maintain training for AI resources.
- Review and assess requests for AI tools and resources and insure adherence to UNT System regulations, policies, standards, and procedures.
- Maintain customer and public\-facing information regarding AI tools and capabilities.
Physical Requirements
- Communicating with others to exchange information.
Environmental Hazards
- No adverse environmental conditions expected.
Work Schedule
Generally, M\-F, 8am \- 5pm
Driving University Vehicle
No
Security Sensitive
This is a Security Sensitive Position.
Special Instructions
Applicants must submit a minimum of two professional references as part of their application. If needed, additional references can be added after the application has been submitted.
Benefits
For information regarding our Benefits, click here.
EEO Statement
The University of North Texas System is firmly committed to equal opportunity and does not permit –and takes actions to prevent – discrimination, harassment (including sexual violence, domestic violence, dating violence and stalking), and retaliation on the basis of race, color, religion, national origin, sex, age, disability, genetic information, or veteran status in its application, employment practices, and facilities; nor permits race, color, national origin, religion, age, disability, veteran status, or sex discrimination and harassment in its admissions processes, and educational programs and activities. UNT System Administration promptly investigates complaints of discrimination, harassment, and related retaliation and takes remedial action when appropriate. System Administration also takes actions to prevent retaliation against individuals who oppose any form of harassment or discriminatory practice, file a charge or report, or testify, assist, or participate in a related investigation or proceeding.
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 University of North Texas, 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 $218,750 based on 3,817 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $120,000.
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.
University of North Texas AI Hiring
University of North Texas has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Denton, TX, US.
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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