Description

Own the end-to-end MLOps lifecycle, from scalable data pipelines to model deployment and monitoring.

This role is hybrid.

Responsibilities

• Implement MLflow for parameter, metric, and artifact management with end-to-end lineage tracking.
• Build and maintain scalable data pipelines for training, validation, and inference using Spark, Hive, and Airflow.
• Develop custom evaluation metrics, explainability components, and fairness/bias testing frameworks.
• Package models for deployment and manage lifecycle transitions across environments on AWS SageMaker and Domino.
• Collaborate with data scientists and engineering teams to ensure compliance and operational readiness.

Required Skills

• 15+ years of relevant experience in data engineering or MLOps.
• Strong proficiency in Python and SQL with deep knowledge of data modeling.
• Hands-on expertise with AWS SageMaker SDKs and Domino platform.
• Experience implementing MLOps practices, including Git-based workflows and version control.
• Proficiency in MLflow for model tracking and management.
• Experience with scalable data processing tools like Spark and Hive.
• Bachelor’s degree in Computer Science, Information Systems, or related field.

Preferred Skills

• Post-graduate degree in a technical field.
• Professional certifications in AWS or data engineering.

Key Skills

Education

Bachelor's degree