AI Infrastructure Engineer

Posted:
7/29/2026, 5:02:53 AM

Location(s):
Austin, Texas, United States ⋅ Texas, United States

Experience Level(s):
Junior ⋅ Mid Level ⋅ Senior

Field(s):
AI & Machine Learning ⋅ DevOps & Infrastructure ⋅ Software Engineering

Workplace Type:
Remote

Pay:
$100k/yr

Job Posting Title:

AI Infrastructure Engineer

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Hiring Department:

ET - Initiatives and Instructional Technology

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Position Open To:

All Applicants

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Weekly Scheduled Hours:

40

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FLSA Status:

Exempt from FLSA

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Earliest Start Date:

Immediately

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Position Duration:

Expected to Continue Until Sep 01, 2029

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Location:

AUSTIN, TX

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Job Details:

General Notes

This is a fixed term position that ends three years from the employee’s start date.

 

Flexible work arrangements are available for this position, including the ability to work remotely at least 50% of the time within the United States. In-person meetings will be required when applicable. We prefer a candidate located in the greater Austin area as travel to campus for occasional in-person events, training, team meetings, activities, etc., will be required.

 

This position provides a balance of life/work with typically a 40-hour work week, and travel limited to training (e.g., conferences/courses).

 

Enterprise Technology is dedicated to supporting the mission of the University of Texas at Austin of unlocking potential and preparing future leaders of the state.

Your skills will make a difference.

You’ll be working for a university that is internationally recognized for research and the work you do will make a difference in the lives of our students, faculty and staff. If you’re the type of person that wants to know your work has meaning and impact, you’ll like working for our campus.

 

The University of Texas at Austin and Enterprise Technology provide an outstanding benefits package to our staff. Those benefits include:

 

  • Competitive health benefits (Employee premiums covered at 100%, family premiums at 50%)

  • Vision, Dental, Life, and Disability insurance options

  • Paid vacation, sick leave, and holidays

  • Teachers Retirement System of Texas (a defined benefit retirement plan)

  • Additional Voluntary Retirement Programs: Tax Sheltered Annuity 403(b) and a Deferred Compensation program 457(b)

  • Flexible spending account options for medical and childcare expenses

  • Training and conference opportunities

  • Tuition assistance

  • Athletic ticket discounts

  • Access to UT Austin's libraries and museums

  • Free rides on all UT Shuttle and Capital metro buses with staff ID card

For more details, please see: https://hr.utexas.edu/prospective/benefits and https://hr.utexas.edu/current/services/my-total-rewards

Must be authorized to work in the United States on a full-time basis for any employer without sponsorship.

 

This position requires you to maintain internet service and a mobile phone with voice and data plans to be used when required for work.

Purpose

Enterprise Technology's AI Studio serves as the central hub for The University of Texas at Austin's artificial intelligence initiatives and works to expand universal access to AI tools and services across campus. The AI Infrastructure Engineer designs, builds, and maintains the infrastructure underpinning the university's enterprise AI ecosystem.

 

This position supports core AI services and infrastructure used by faculty, staff, and students—including ChatGPT, Claude, Gemini, Microsoft Copilot, Azure AI Foundry, Power Automate, the university's AI Gateway, Portkey, and related AI development and integration tools. The AI Infrastructure Engineer ensures these services are scalable, resilient, secure, and performant across a wide range of academic, research, and administrative use cases.

Working closely with AI engineers, AI educators, campus IT teams, and student innovators, this role translates emerging AI capabilities into robust, production-grade infrastructure that supports teaching, research, and institutional operations. The position also leads documentation, playbooks, and operational processes that enable responsible, secure, and effective AI adoption across the university community

Responsibilities

AI Platform Infrastructure, Integration and Operations:

  • Design, deploy, and maintain infrastructure for AI platforms and services offered through the AI Studio including solutions built within AWS, Microsoft Azure, Google GCP, OpenAI, Anthropic, and other cloud-based AI vendors.
  • Architect and manage CI/CD pipelines, container orchestration (Kubernetes/Docker), and infrastructure-as-code workflows supporting AI service delivery.
  • Troubleshoot infrastructure and platform issues involving AI services, APIs, MCPs, authentication systems, integrations, and development environments.
  • Manage service health monitoring, alerting, and incident response for AI platforms and services; coordinate escalations with engineering and vendor teams.
  • Configure and integrate AI tools within campus systems, applications, and research workflows.
  • Maintain and evolve shared AI infrastructure including GPU-enabled workstations, AI development environments, and HPC resources used by students and researchers.
  • Develop and maintain runbooks, infrastructure documentation, and architectural diagrams for institutional AI services.
  • Lead testing, validation, and rollout of new AI tools and services prior to campus deployment.
  • Design and operate a campus-wide pipeline enabling faculty, staff, and students to build and deploy AI-assisted and AI-generated software to cloud infrastructure seamlessly, including automated code review gates, container image signing, environment promotion workflows, and integration with university cloud tenants (Azure, AWS, GCP).
  • Build and maintain automation and workflow infrastructure for tools such as Microsoft Power Automate that incorporate AI capabilities.
  • Contribute to platform security posture, access controls, and compliance practices for AI services.

Campus AI Infrastructure and Enablement:

  • Partner with faculty, staff, and students to design scalable infrastructure solutions that support AI adoption in teaching, research, and operational workflows.
  • Support AI Studio workshops, demonstrations, and technical sessions on enterprise AI infrastructure and deployment practices.
  • Develop technical documentation, architecture guides, and infrastructure runbooks to support AI literacy and operational readiness.
  • Help lower barriers to entry for AI tools by providing robust, well-documented infrastructure accessible across a wide range of technical backgrounds.
  • Advise academic and administrative units on infrastructure requirements for AI tools and services.
  • Support student-led AI innovation activities and experimentation environments within the AI Studio.

AI Service Reliability, Monitoring and Continuous Improvement:

  • Track infrastructure issues, service incidents, and user feedback to identify trends and opportunities to improve AI platform reliability.
  • Monitor usage patterns, performance metrics, and cost efficiency across supported AI platforms.
  • Collaborate with engineering teams and service owners to relay operational insights and inform improvements to AI tools and services.
  • Evaluate emerging AI platforms, cloud services, and DevOps tooling to inform future campus infrastructure decisions.

Perform Other Related Functions as Assigned:

  • Contribute to cross-functional initiatives related to AI governance, responsible AI practices, and service reliability.
  • Participate in operational meetings, service planning sessions, and collaboration with Enterprise Technology teams.
  • Maintain and improve internal processes that support sustainable AI service delivery at scale.
  • Stay informed on emerging AI infrastructure patterns, DevOps methodologies, and enterprise AI services.
  • Assist with development of training materials and informational resources that promote AI literacy and infrastructure best practices across campus.

Required Qualifications

  • Bachelor's degree in Computer Science, Information Systems, Software Engineering, or a related field. Relevant work experience may substitute for education.
  • Experience in DevOps, site reliability engineering (SRE), platform engineering, or cloud infrastructure roles.
  • Hands-on experience with containerization and orchestration technologies such as Docker and Kubernetes.
  • Experience with CI/CD pipelines and infrastructure-as-code tools (e.g., Terraform, Ansible, Helm, GitHub Actions).
  • Familiarity with cloud platforms such as Microsoft Azure, AWS, or Google Cloud.
  • Experience deploying or operating AI/ML platforms, generative AI services, or LLM inference infrastructure.
  • Strong understanding of networking, authentication, API management, and cloud security fundamentals.
  • Experience with monitoring, logging, and observability tooling (e.g., Prometheus, Grafana, Datadog, Azure Monitor).
  • Experience and extensive domain knowledge of Identity and Access Management concepts including Shibboleth, oidc, oauth2, and scim provisioning.
  • Strong problem-solving and analytical skills with a systems thinking mindset.
  • Excellent written and verbal communication skills; ability to explain technical concepts to varied audiences.
  • Demonstrated ability to collaborate effectively with campus communities including faculty, staff, and students.

Equivalent combination of relevant education and experience may be substituted as appropriate.

Preferred Qualifications

  • Experience operating enterprise AI platforms such as Azure AI Foundry, ChatGPT Enterprise, Claude for Enterprise, or Google Gemini.
  • Familiarity with AI gateway or LLM proxy tools such as Portkey, LiteLLM, or similar platforms.
  • Experience supporting GPU-enabled infrastructure for LLM inference (e.g., vLLM, NVIDIA CUDA environments, Blackwell/ARM64 hardware).
  • Experience with Microsoft Copilot, Microsoft 365 automation, or Power Platform infrastructure.
  • Proficiency in scripting or programming languages such as Python, Bash, or JavaScript/TypeScript.
  • Experience using Ansible or other orchestration layer tooling.
  • Experience with vector databases, embeddings pipelines, or AI data infrastructure (e.g., Azure Cosmos DB with DiskANN, Pinecone, Weaviate).
  • Familiarity with Jupyter notebooks, Git, collaborative development environments, and MLOps workflows.
  • Experience working in higher education, research computing, or academic technology environments.
  • Interest in responsible AI practices, AI governance, and ethical AI infrastructure design.

Salary Range

$100,000 + depending on qualifications

Working Conditions

  • May work around standard office conditions
  • Repetitive use of a keyboard at a workstation
  • Use of manual dexterity

Work Shift

Monday – Friday, flexible between 7am-6pm

Required Materials

  • Resume/CV
  • 3 work references with their contact information; at least one reference should be from a supervisor
  • Letter of interest

Important for applicants who are NOT current university employees or contingent workers: You will be prompted to submit your resume the first time you apply, then you will be provided an option to upload a new Resume for subsequent applications. Any additional Required Materials (letter of interest, references, etc.) will be uploaded in the Application Questions section; you will be able to multi-select additional files. Before submitting your online job application, ensure that ALL Required Materials have been uploaded.  Once your job application has been submitted, you cannot make changes.

Important for Current university employees and contingent workers: As a current university employee or contingent worker, you MUST apply within Workday by searching for Find UT Jobs. If you are a current University employee, log-in to Workday, navigate to your Worker Profile, click the Career link in the left hand navigation menu and then update the sections in your Professional Profile before you apply. This information will be pulled in to your application. The application is one page and you will be prompted to upload your resume. In addition, you must respond to the application questions presented to upload any additional Required Materials (letter of interest, references, etc.) that were noted above.

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Employment Eligibility:

Regular staff who have been employed in their current position for the last six continuous months are eligible for openings being recruited for through University-Wide or Open Recruiting, to include both promotional opportunities and lateral transfers. Staff who are promotion/transfer eligible may apply for positions without supervisor approval.

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Retirement Plan Eligibility:

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.

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Background Checks:

A criminal history background check will be required for finalist(s) under consideration for this position.

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Equal Opportunity Employer:

The University of Texas at Austin, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions.

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Pay Transparency:

The University of Texas at Austin will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information.

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Employment Eligibility Verification:

If hired, you will be required to complete the federal Employment Eligibility Verification I-9 form.  You will be required to present acceptable and original documents to prove your identity and authorization to work in the United States.  Documents need to be presented no later than the third day of employment.  Failure to do so will result in loss of employment at the university.

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E-Verify:

The University of Texas at Austin use E-Verify to check the work authorization of all new hires effective May 2015. The university’s company ID number for purposes of E-Verify is 854197. For more information about E-Verify, please see the following:

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Compliance:

Employees may be required to report violations of law under Title IX and the Jeanne Clery Disclosure of Campus Security Policy and Crime Statistics Act (Clery Act). If this position is identified a Campus Security Authority (Clery Act), you will be notified and provided resources for reporting. Responsible employees under Title IX are defined and outlined in HOP-3031.

The Clery Act requires all prospective employees be notified of the availability of the Annual Security and Fire Safety report. You may access the most recent report here or obtain a copy at University Compliance Services, 1616 Guadalupe Street, UTA 2.206, Austin, Texas 78701.

University of Texas at Austin

Website: https://www.utexas.edu/

Headquarter Location: Austin, Texas, United States

Employee Count: 10001+

Year Founded: 1883

IPO Status: Private

Last Funding Type: Grant

Industries: Education ⋅ Higher Education ⋅ Universities

Visa Sponsorship: Sponsors work visas