Description

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

This role is remote.

Responsibilities

• Design and deploy scalable infrastructure for ML workloads using cloud platforms and containerization technologies.
• Build automated pipelines to run, monitor, and retrain ML models for business applications.
• Implement model and pipeline validation procedures alongside Data Scientists and Data Engineers.
• Create and maintain CI/CD pipelines for continuous enhancement in development and production environments.
• Optimize and refactor development code for stable production deployment.

Required Skills

• 5+ years of professional engineering experience.
• Strong proficiency with Azure Cloud and Azure DevOps.
• Hands-on experience with Kubernetes and Azure Kubernetes Service (AKS).
• Proven track record implementing MLOps practices.
• Experience building and managing CI/CD pipelines.
• Working knowledge of Databricks.
• Experience integrating and deploying trained ML models.

Preferred Skills

• Background in data engineering or data science workflows.

Key Skills

Education

Any Graduate