Description

You will manage the end-to-end machine learning lifecycle from data pre-processing to production monitoring.

This role is on-site.

Responsibilities

• Build and maintain scalable MLOps infrastructure for model versioning and automated deployment.
• Refactor notebooks into high-quality code scripts for integration into production ML pipelines.
• Design and optimize ML pipelines to streamline model development and deployment.
• Implement repeatable CI/CD pipelines including data, model, and code testing.
• Mentor Data Science teams and develop assets for the internal MLOps community.

Required Skills

• 3+ years of experience in machine learning environments.
• Proficiency in Python.
• Strong SQL skills.
• Experience with Azure (DevOps, Services, SQL, Azure Search).
• Hands-on experience with ML frameworks including MLflow, Databricks, and Kubeflow.
• Experience implementing MLOps, LLMOps, and automated deployment workflows.
• Bachelor's degree in Computer Science.

Preferred Skills

• Experience codifying best practices for model performance monitoring, drift detection, and automated retraining.

Key Skills

Education

Bachelor's degree in Computer Science