IT Support

Provide a summary of the job's primary function:

*** is seeking a detail-oriented and proactive AI Operations Specialist to join our IT team. The AI Operations Specialist will be responsible for the day-to-day management, monitoring, and operational support of the university's AI systems and data pipelines across various departments. This role is vital in ensuring AI solutions and their supporting data infrastructure function reliably, meet performance expectations, and continuously improve to deliver maximum value. The position requires expertise in MLOps practices, data pipeline operations, system monitoring, incident management, and continuous improvement of AI systems in production environments.
24/7 business continuity:
This role requires availability outside of traditional working hours on a rotating basis to ensure continuous operation of critical AI systems and data pipelines. Responsibilities include monitoring system health, responding to alerts, troubleshooting performance issues, and implementing emergency fixes as needed. The ideal candidate must be able to quickly diagnose and resolve AI system and data pipeline incidents, prioritize issues based on business impact, and coordinate with technical teams to restore service. A strong commitment to system reliability and service continuity is essential for success in this position.
Other duties as required:
This role requires flexibility in performing duties outside of the primary responsibilities to support the evolving AI ecosystem at the university. The ideal candidate must be adaptable and willing to take on additional tasks or projects as required, ensuring consistent and reliable AI and data pipeline operations. This may include assisting with knowledge management, documentation updates, user training, data preparation, or special projects related to AI system improvements. A problem-solving mindset and willingness to tackle emerging challenges are essential for thriving in this dynamic environment.
Hybrid work schedule:
This role is hybrid and in the office a minimum of three days a week to facilitate collaboration with both technical teams and operations staff. In-office presence enables effective coordination with support teams, direct access to infrastructure, and hands-on troubleshooting of AI systems and data pipelines. Physical presence is particularly important for incident response, change management activities, and cross-functional problem-solving sessions that benefit from in-person collaboration and real-time communication.

1. Minimum Qualifications
MLOps Experience: Demonstrated experience in operationalizing and maintaining machine learning models in production environments, including deployment, monitoring, and lifecycle management.
Data Pipeline Operations: Extensive experience maintaining and troubleshooting data pipelines built with tools like Apache Airflow, Prefect, cloud data services (AWS, Azure, Google Cloud Platform), and data processing frameworks (Spark, Kafka), ensuring reliable data flow for AI systems.
System Monitoring: Proficiency in monitoring AI system and data pipeline performance, detecting anomalies, and implementing proactive measures to ensure system reliability and availability.
Incident Management: Strong experience in troubleshooting, diagnosing, and resolving AI system and data infrastructure issues, with the ability to prioritize incidents based on business impact.
Performance Optimization: Knowledge of techniques to optimize AI system and data pipeline performance, including resource allocation, scaling strategies, and performance tuning.
Change Management: Experience implementing changes to production AI systems and data pipelines with minimal disruption, including testing, validation, and rollback procedures.
Data Quality Management: Understanding of data quality principles and their impact on AI system performance, with the ability to identify and address data-related issues in processing pipelines.
Documentation and Knowledge Management: Excellence in creating and maintaining operational documentation, runbooks, and knowledge articles for AI systems and data pipelines.
utomation Skills: Ability to create and implement automation scripts and workflows to streamline routine operational tasks for both AI systems and data flows, enhancing overall system reliability.
DevOps Practices: Familiarity with DevOps and CI/CD principles as applied to AI systems and data pipelines, including containerization, orchestration, and infrastructure as code.
Security Awareness: Understanding of security best practices for AI operations and data handling, including access control, data protection, and vulnerability management.
Collaboration Skills: Strong ability to work with cross-functional teams, communicate technical concepts clearly, and coordinate incident response activities effectively.
Problem-solving: Excellent analytical and problem-solving skills, with the ability to troubleshoot complex issues in AI systems and data infrastructure in a methodical and efficient manner.
Compliance Knowledge: Understanding of relevant regulations and compliance requirements affecting AI systems and data processing in higher education environments.
Communication Skills: Clear and concise communication abilities, both written and verbal, to document procedures, report incidents, and coordinate with stakeholders.
Service Management: Knowledge of IT service management principles and frameworks, with experience applying them to AI and data pipeline operations.
Bachelor's degree in Computer Science, Information Technology, or related field; technical certifications in relevant areas (e.g., cloud platforms, MLOps, data engineering) preferred.
Minimum of 3 years of experience in IT operations, with at least 1 year focused on AI/ML systems and data pipeline support.
Experience with cloud platforms (AWS, Azure, or Google Cloud Platform) and their AI/ML and data engineering service offerings.
2. Key Responsibilities & Accountabilities
Identify the most important job duties (maximum of 5) using no more than 3-4 concise sentences. Indicate the typical percent of time required for each job duty; the total percent of time must equal 100%. Begin with the most important duty.
Percent of Time
1
System Monitoring and Incident Management
Monitor AI system and data pipeline health, performance, and availability using established monitoring tools and dashboards. Detect, triage, and resolve incidents affecting AI systems and their data infrastructure, coordinating with technical teams as needed. Implement proactive measures to prevent recurring issues and minimize service disruptions.
35%
2
Operational Support and Maintenance
Perform routine operational tasks to maintain AI systems and data pipelines, including model updates, data refreshes, pipeline maintenance, and system patches. Implement scheduled maintenance activities with minimal service disruption. Manage user access and permissions for AI platforms according to security policies.
25%

20%

10%
3
Performance Analysis and Optimization
Analyze AI system and data pipeline performance metrics, identify bottlenecks and inefficiencies, and implement optimizations to improve response times, data flow, accuracy, and resource utilization. Monitor for model drift and data quality issues, coordinating retraining or pipeline adjustments when necessary.
4
Documentation and Knowledge Management
Create and maintain comprehensive operational documentation, including runbooks, standard operating procedures, and knowledge base articles. Document system configurations, data pipeline dependencies, and recovery procedures to ensure operational continuity.
5
Continuous Improvement and Automation
Identify opportunities for process improvement and automation in AI operations. Develop and implement scripts and workflows to automate routine tasks, reducing manual effort and minimizing human error. Contribute to the evolution of MLOps practices based on operational experience and emerging best practices.
10%
Hybrid 3 days onsite