Roles and Responsibilities

• Design, develop, and automate end-to-end ML pipelines, from data ingestion to model deployment and monitoring.
• Implement automation for scheduling, monitoring, status notifications, and resiliency, ensuring efficient pipeline execution and failure recovery.
• Work closely with cross-functional teams, including analysts, data engineers, data scientists, and visualization developers to align ML pipeline development with overall solution delivery.
• Write clean, efficient, and maintainable code that adheres to best practices and ensures long-term scalability.
• Create and maintain comprehensive documentation for the development, deployment, and operation of ML pipelines.
• Investigate and resolve production issues in a timely manner, ensuring smooth operation of pipelines.
• Participate in code reviews, providing constructive feedback and guidance to fellow engineers.
• Actively participate in agile ceremonies (e.g., daily standups, sprint planning, and retrospectives) to ensure continuous improvement and alignment with project objectives.

Required Qualifications

• Proven experience in developing ML pipelines using Python and relevant ML libraries (e.g., pandas, scikit-learn).
• Significant hands-on experience with developing and deploying ML pipelines in cloud environments (GCP preferred, but experience with AWS or Azure is also relevant).
• Strong verbal and written communication skills, with the ability to engage effectively with both technical and non-technical team members.
• Hands-on experience in agile methodologies, particularly Scrum or Kanban.
• Expertise with CI/CD tools and workflows, especially GitHub Actions or similar platforms.
• In-depth experience with orchestration tools like Apache Airflow for scheduling and managing ML workflows.

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