Description

You will lead the migration and optimization of ML models from GCP to Azure Databricks, ensuring scalable, cost-efficient execution.

This role is on-site.

Responsibilities

• Migrate existing ML models from GCP to Azure Databricks, replicating and optimizing architectures for performance.
• Build and manage CI/CD pipelines using GitHub Actions for automated ML model deployments.
• Implement MLflow for model tracking, versioning, and lifecycle management.
• Develop scalable Data and ML pipelines using Databricks and PySpark.
• Collaborate with Data Engineering to automate cross-cloud data movement between GCP and Azure.

Required Skills

• 8+ years of experience in Data Engineering, ML Engineering, or MLOps.
• Deep hands-on expertise in Databricks internals.
• Proficiency in PySpark, including execution plan analysis and optimization.
• Experience building CI/CD pipelines with GitHub Actions.
• Strong knowledge of MLflow for operationalizing models.
• Ability to integrate workflows between GCP and Azure ecosystems.
• Data Engineering and ML Engineering background.

Preferred Skills

• Experience with cross-cloud data movement patterns.
• Familiarity with DS model structures and monitoring.

Key Skills

Education

Not specified