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About the Role

Enterprise Technology's AI Studio is the central hub for The University of Texas at Austin's artificial intelligence initiatives, expanding access to AI tools and services across campus. The AI Infrastructure Engineer designs, builds, and maintains the infrastructure for the university's enterprise AI ecosystem, supporting core AI services and infrastructure used by faculty, staff, and students. This role ensures services are scalable, resilient, secure, and performant, translating emerging AI capabilities into robust, production-grade infrastructure.

Responsibilities

Design, deploy, and maintain infrastructure for AI platforms and services 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.
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.
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.
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.
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.

Requirements

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.
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.

Skills

DevOps
Site Reliability Engineering (SRE)
Platform Engineering
Cloud Infrastructure
Docker
Kubernetes
CI/CD pipelines
Infrastructure-as-code (Terraform, Ansible, Helm, GitHub Actions)
Microsoft Azure
AWS
Google Cloud
AI/ML platforms
Generative AI services
LLM inference infrastructure
Networking
Authentication
API management
Cloud security
Monitoring
Logging
Observability tooling (Prometheus, Grafana, Datadog, Azure Monitor)
Identity and Access Management (Shibboleth, oidc, oauth2, scim provisioning)
Problem-solving
Analytical skills
Systems thinking
Written communication
Verbal communication
Collaboration
Azure AI Foundry
ChatGPT Enterprise
Claude for Enterprise
Google Gemini
AI gateway tools (Portkey, LiteLLM)
GPU-enabled infrastructure for LLM inference
Microsoft Copilot
Microsoft 365 automation
Power Platform infrastructure
Python
Bash
JavaScript/TypeScript
Ansible
Vector databases
Embeddings pipelines
AI data infrastructure
Jupyter notebooks
Git
MLOps workflows
Responsible AI practices
AI governance
Ethical AI infrastructure design

Location

AUSTIN, TX

Work Type

Remote (at least 50% of the time within the United States)
Hybrid
Full-time

Experience Level

Mid-level

Education Level

Bachelor's degree in Computer Science, Information Systems, Software Engineering, or a related field

Salary/Compensations

$100,000 +

Benefits

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

About the Company

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.

Equal Opportunity

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.