Mondo Staffing
Apply now: \n
AI Operations Specialist
, location is \n
Onsite
. The start date is \n
ASAP
for this \n
long term contract
position.\n \n \n
Job Title:
AI Operations Specialist\n \n
Location-Type:
Boston, MA - Hybrid 3 days onsite/week\n \n
Start Date Is:
August 2025\n \n
Duration:
1 year contract \n \n
Compensation Range:
$62/hr - to $97/hr \n \n \n
Job Description: \n Northeastern University 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.\n \n \n
Requirements: \n \n 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. \n
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. \n
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. \n
Create and maintain comprehensive operational documentation, including runbooks, standard operating procedures, and knowledge base articles. \n
Document system configurations, data pipeline dependencies, and recovery procedures to ensure operational continuity. \n
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. \n \n \n
Minimum Qualifications
\n
\n
Bachelor's degree in Computer Science, Information Technology, or related field; technical certifications in relevant areas (e.g., cloud platforms, \n
MLOps, data engineering) preferred. \n
Minimum of 3 years of experience in IT operations, with at least 1 year focused on AI/ML systems and data pipeline support. \n
Experience with cloud platforms (AWS, Azure, or GCP) and their AI/ML and data engineering service offerings. \n
AI Operations Specialist
, location is \n
Onsite
. The start date is \n
ASAP
for this \n
long term contract
position.\n \n \n
Job Title:
AI Operations Specialist\n \n
Location-Type:
Boston, MA - Hybrid 3 days onsite/week\n \n
Start Date Is:
August 2025\n \n
Duration:
1 year contract \n \n
Compensation Range:
$62/hr - to $97/hr \n \n \n
Job Description: \n Northeastern University 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.\n \n \n
Requirements: \n \n 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. \n
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. \n
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. \n
Create and maintain comprehensive operational documentation, including runbooks, standard operating procedures, and knowledge base articles. \n
Document system configurations, data pipeline dependencies, and recovery procedures to ensure operational continuity. \n
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. \n \n \n
Minimum Qualifications
\n
\n
Bachelor's degree in Computer Science, Information Technology, or related field; technical certifications in relevant areas (e.g., cloud platforms, \n
MLOps, data engineering) preferred. \n
Minimum of 3 years of experience in IT operations, with at least 1 year focused on AI/ML systems and data pipeline support. \n
Experience with cloud platforms (AWS, Azure, or GCP) and their AI/ML and data engineering service offerings. \n