AI Infrastructure Engineer

AI Infrastructure Engineer

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 AI tools and services. 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, 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.

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