Description

You will own the infrastructure and deployment lifecycle for ML applications.

Responsibilities

• Design and deploy scalable infrastructure for ML workloads using cloud platforms and containerization technologies.
• Build cloud-hosted, automated pipelines that run, monitor, and retrain ML Models for business applications.
• Design and implement Model and Pipeline validation procedures with Data Scientists and Data Engineers.
• Optimize and refactor development code for production deployment.
• Create and develop CI/CD Pipelines for continuous enhancement in development and production.

Required Skills

• 5+ years of professional experience.
• Kubernetes and AKS.
• Azure Cloud and Azure DevOps.
• MLOps practices.
• CI/CD Pipelines development.
• Databricks experience.
• Experience integrating and deploying trained ML Models.

Key Skills

Education

Any Graduate