Job Description

Job Summary
Position will be hired as a Systems Engineer III and will work as an Enterprise AI Engineer to design, deploy, and operate artificial intelligence solutions that improve university services and decision-making. The position combines AI engineering with production observability so that models, applications, data pipelines, and supporting platforms are measurable, secure, scalable, and reliable. Working across development and operations, this role uses Splunk as a core required platform to provide logging, dashboards, alerting, incident insight, and performance visibility for enterprise AI and related services.
Minimum Education and Experience
Bachelor’s degree and four (4) years of related experience or an equivalent combination of relevant education and experience may be considered.
Preferred Education and Experience
Master’s degree in computer science, data science, information technology, or a related field, with experience implementing AI or enterprise technology solutions in a complex organization or higher education environment.
Hands-on experience with Retrieval-Augmented Generation, agentic AI, prompt engineering, model evaluation, and responsible AI practices.
Experience with current AI platforms and services such as Microsoft 365 Copilot, Azure AI Foundry, Amazon Bedrock, Amazon SageMaker, OpenAI, Claude, Gemini, Google Vertex AI, or Amazon Q.
Demonstrated experience administering or engineering Splunk, including data onboarding, search processing language, dashboards, alerts, reports, index management, and troubleshooting.
Experience designing observability solutions across applications and infrastructure using logs, metrics, traces, service health indicators, and event correlation.
Experience with monitoring, AIOps, or infrastructure platforms such as Dynatrace, Zabbix, cloud-native monitoring services, or equivalent technologies.
Experience with cloud platforms, containers, CI/CD pipelines, infrastructure automation, and tools such as Ansible.
Ability to develop clear technical documentation, operational runbooks, knowledge articles, and presentations for technical and nontechnical audiences.
Experience engaging faculty, staff, students, vendors, and cross-functional technical teams and facilitating workshops or technical reviews.
Strong leadership, mentoring, communication, troubleshooting, and incident-management capabilities.
Integrity and sound judgment when handling confidential, regulated, or security-sensitive university information.
Other Qualifications
To the extent this position requires the holder to research, work on, or have access to critical infrastructure as defined in Section 117.001(2) of the Texas Business and Commerce Code, the ability to maintain the security or integrity of the critical infrastructure is a minimum qualification to be hired and to continue to be employed in the position.
Essential Duties and Responsibilities
Follow ethical, responsible, secure, and privacy-conscious AI practices when designing and operating solutions for UT Dallas.
Translate organizational requirements into scalable AI and generative AI solutions aligned with university strategy, security standards, and operational needs.
Design, develop, deploy, and support AI-driven applications using machine learning, large language models, natural language processing, deep learning, Retrieval-Augmented Generation, chatbots, and agentic AI patterns.
Build production observability into AI services by defining logs, metrics, traces, dashboards, alerts, service health indicators, and operational runbooks.
Provide hands-on Splunk administration and engineering, including installation and upgrades, configuration, data onboarding and parsing, index and retention management, forwarder and deployment server management, role-based access controls, search and knowledge-object management, dashboards, alerts, reports, performance tuning, capacity planning, troubleshooting, and operational support; prior Splunk administration experience is required for this position.
Design and optimize monitoring and logging integrations across AI platforms, applications, cloud services, operating systems, containers, databases, storage, and network infrastructure using Splunk and complementary observability tools.
Apply AIOps techniques to correlate events, identify anomalies, reduce alert noise, support root-cause analysis, and improve the reliability and performance of enterprise services.
Automate deployment, configuration, monitoring, and operational workflows using Python, PowerShell, Bash, Ansible, APIs, and CI/CD practices.
Evaluate AI and observability vendors and technologies based on cost, data protection, security, performance, interoperability, scalability, and supportability.
Review campus requests for AI tools and provide technical recommendations related to architecture, safety, compliance, data processing, integration, monitoring, and long-term operations.
Optimize AI models, prompts, data pipelines, and supporting infrastructure for quality, performance, availability, scalability, and cost efficiency.
Perform testing and evaluation of AI models and services, including functional quality, security, latency, reliability, resource utilization, and production readiness.
Support enterprise environments including Claude, OpenAI, Gemini, GitHub, Microsoft 365 Copilot, CometAI, Splunk, and related cloud and infrastructure services.
Participate in incident response and on-call activities, using observability data to restore service quickly, document root causes, and implement corrective actions.
Maintain service catalogs, knowledge bases, architecture documentation, monitoring standards, operational procedures, and technical training materials.
Consult with schools and departments, communicate technical concepts to varied audiences, and facilitate workshops on AI, generative AI, and service observability.
Assume technical lead responsibilities, mentor junior engineers, and promote consistent engineering, documentation, and operational practices across the team.
Research emerging AI, machine learning, observability, automation, cloud, and platform engineering technologies and apply relevant advancements to university services.
Physical Demands and Working Conditions
Physical Activities
Working Conditions
Additional Information
Knowledge Skills and Abilities:
Proficiency with Splunk administration and engineering, including SPL, data ingestion, dashboards, alerts, reporting, access controls, performance troubleshooting, and operational support.
Knowledge of machine learning techniques, generative AI architectures, large language models, model evaluation, prompt engineering, and responsible AI practices.
Proficiency in Python and working knowledge of one or more additional languages or scripting tools such as PowerShell, Bash, R, JavaScript, or C++.
Understanding of observability principles, including structured logging, metrics, distributed tracing, alert design, service health, event correlation, root-cause analysis, and capacity planning.
Familiarity with cloud platforms such as AWS, Azure, or Google Cloud and their AI, data, security, and monitoring services.
Knowledge of data engineering concepts, including pipelines, integration, data quality, transformation, warehousing, and working with large datasets.
Working knowledge of Linux and Windows systems, networking concepts, APIs, containers, orchestration, storage, databases, and enterprise infrastructure.
Ability to automate repeatable engineering and operational processes using scripts, configuration management, APIs, and CI/CD tools.
Strong analytical, creative problem-solving, and troubleshooting skills, with the ability to diagnose complex AI, application, data, and infrastructure issues.
Ability to communicate technical information clearly to engineers, business stakeholders, faculty, staff, students, and executive leaders.
Ability to participate in an on-call rotation, manage high-priority incidents, coordinate change activities, and remain effective in time-sensitive situations.
A collaborative, self-motivated, results-oriented approach that values collective success, continuous learning, documentation, and operational excellence.

Remote Work Eligibility Statement
A remote work schedule is not available for this position.

What We Can Offer
UT Dallas is an Equal Opportunity Employer. We offer an employee-friendly work environment with a comprehensive benefits package including:
· Competitive Salary
· Tuition Benefits
· BCBS PPO Medical Insurance Options – including 100% paid employee medical coverage for full-time employees
· AD&D Insurance
· Dental & Vision Plan Options
· Optional Long and Short-term Disability
· TRS Retirement Plan
· Fertility Benefits
· Paid time off