AI Operations Specialist

About the Opportunity
This job description is intended to describe the general nature and level of work being performed by people assigned to this classification. It is not intended to be construed as an exhaustive list of all responsibilities, duties and skills required of personnel so classified.

JOB SUMMARY
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.

This role is hybrid and in the office a minimum of three days a week to facilitate collaboration and teamwork. In-office presence is an essential part of our on-campus culture and allows for engaging directly with staff and students, sharing ideas, and contributing to a dynamic work environment. Being on-site allows for stronger connections, more effective problem-solving, and enhanced team synergy, all of which are key to achieving our collective goals and driving success.

MINIMUM QUALIFICATIONS
Knowledge and skills required for this position are normally obtained through a Bachelor's degree in Computer Science, Information Technology, or related field; technical certifications in relevant areas (e.g., cloud platforms, MLOps, data engineering) preferred and a 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 GCP) and their AI/ML and data engineering service offerings.

Other necessary skills:

• 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, GCP), 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. 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.
• Automation 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.

KEY RESPONSIBILITIES & ACCOUNTABILITIES
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.

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.

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.

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.

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.