Machine Learning Ops and Data Engineer

This role combines data platform engineering and MLOps, supporting analytical data flows and production ML models. The engineer will maintain ETL pipelines, deploy and monitor ML models, and collaborate with Data Science and Engineering teams.

• Deploy machine learning models into production environments and support their operational lifecycle.
• Support cloud-based analytical, reporting, and machine learning infrastructure.
• Collaborate closely with Data Science, Engineering, Risk, BI, and IT teams to align data and model requirements with production standards.
• Develop automation for model deployment, updates, scaling, and recurring data processing tasks.
• Implement monitoring for both data pipelines and machine learning models, including performance, availability, and quality checks.
• Ensure reliable operation and continuous development of the analytical data warehouse environment.
• Design, maintain, troubleshoot, and optimize ETL/data pipelines supporting reporting, analytics, and machine learning use cases.
• Ensure timely and high-quality data availability for BI, Risk, Data Science, and other business stakeholders.
• Identify, investigate, and resolve performance issues across data warehouse, ETL, and model deployment processes.
• Troubleshoot, debug, upgrade, and improve existing software, pipelines, and deployment processes.