Description

You will own the hybrid ML infrastructure, connecting on-premises open-source tools with cloud platforms like Databricks. You design and maintain pipelines that enable experiment tracking, model versioning, and lifecycle management using MLflow.

Responsibilities

• Implement and maintain hybrid ML infrastructure bridging on-premises tools with Databricks.
• Configure MLflow servers and APIs for secure integration with data sources and deployment targets.
• Manage registration, versioning, and deployment of machine learning models across environments.
• Optimize data pipelines and deployment processes for reproducibility, security, and scalability.
• Collaborate with data scientists to streamline workflows and ensure reliable model deployment.

Required Skills

• 5+ years of experience in data engineering and ML infrastructure.
• Proven expertise with MLflow and Databricks.
• Strong proficiency in Python and Apache Spark.
• Experience managing machine learning models in hybrid (on-prem/cloud) environments.
• Deep understanding of data engineering principles and model lifecycle management.
• Hands-on experience with Linux/Unix systems.
• Ability to work effectively with data scientists and ML engineers.

Key Skills

Education

Any Gradute