Machine Learning Ops Engineer

Job Summary

The AI Operations Production Support Specialist, 2 is responsible for ensuring the stability, performance, and availability of production systems through the application of Artificial Intelligence for IT Operations (AIOps) tools and methodologies. This role involves managing and optimizing AIOps platforms to proactively detect, diagnose, and resolve operational issues before they impact the business. The Support Specialist automates monitoring and support tasks, drives continuous improvements, and works closely with cross-functional teams to maintain efficient production operations. The position requires a strong background in IT operations, incident management, automation, and a deep understanding of AIOps technologies to enhance overall system reliability and efficiency.

Essential Job Functions

• Provide L1, L2, and L3 support for AIOps products.
• Monitor and support operations, addressing client queries and ensuring smooth functionality, execution and troubleshooting.
• Analyze and interpret data effectively.
• Develop and maintain Python scripts.
• Independently understand and address client requirements and queries.
• Perform detailed data analysis to support decision-making processes.
• Collaborate with different teams to implement models and monitor outcomes.
• Provide updates to stakeholders on status of deployments and any risks and issues.
• Keep up to date with the latest technology trends.
• Continuously improve models and techniques to adapt to new data patterns and trends.

Minimum Qualifications

• Bachelors Degree in Computer Science, Engineering, or related field of study.
• 2+ years experience as a Production Support Analyst in AI Operations, Machine Learning Engineering, Data Science, Data Engineering, DevOps, Analytics, or related fields, with a focus in retail financial services or information technology fields utilizing agile methodologies.

Preferred Qualifications

• 5+ years of experience in AI Operations, Machine Learning Engineering, Data Science, Data Engineering, DevOps, Analytics, or related fields.