Apply on Employer Site

Northeastern University · 5 days ago

AI Operations Specialist

Boston, MA

Full-time

Hybrid

Mid Level

3+ years exp

Northeastern University is seeking an AI Operations Specialist to manage and support the university's AI systems and data pipelines. This role involves ensuring the reliability and performance of AI solutions while implementing operational improvements and automation practices.

Education

Growth Opportunities

No H1B

Responsibilities

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

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

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

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

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

Qualification

MLOpsData Pipeline OperationsSystem MonitoringCloud PlatformsPerformance OptimizationAutomation SkillsDevOps PracticesSecurity AwarenessDocumentation ManagementChange ManagementData Quality Management

Required

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

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

Demonstrated experience in operationalizing and maintaining machine learning models in production environments, including deployment, monitoring, and lifecycle management

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)

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

Knowledge of techniques to optimize AI system and data pipeline performance, including resource allocation, scaling strategies, and performance tuning

Experience implementing changes to production AI systems and data pipelines with minimal disruption, including testing, validation, and rollback procedures

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

Excellence in creating and maintaining operational documentation, runbooks, and knowledge articles for AI systems and data pipelines

Ability to create and implement automation scripts and workflows to streamline routine operational tasks for both AI systems and data flows

Familiarity with DevOps and CI/CD principles as applied to AI systems and data pipelines, including containerization, orchestration, and infrastructure as code

Understanding of security best practices for AI operations and data handling, including access control, data protection, and vulnerability management