Job Description
- 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.
- 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.
Requirement:
- 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 GCP) and their AI/ML and data engineering service offerings.