Description

You build and maintain the infrastructure for machine learning model lifecycle management.

This role is on-site.

Responsibilities

• Design and implement ML workflows using orchestration tools.
• Automate deployment pipelines for ML models in production environments.
• Manage and scale ML infrastructure across cloud platforms.
• Monitor ML systems for performance and drift using established tooling.

Required Skills

• 11+ years of experience in ML Engineering, DevOps, or Infrastructure Engineering focused on ML workflows.
• Proficiency with cloud platforms including AWS, GCP, and Azure.
• Expertise with orchestration tools such as Kubernetes and Airflow.
• Strong programming skills in Python.
• Experience using infrastructure-as-code tools like Terraform and Helm.
• Solid understanding of CI/CD practices.
• Familiarity with MLOps frameworks.

Preferred Skills

• Experience with model monitoring and drift detection tools.
• Background in software engineering principles applied to ML systems.

Key Skills

Education

Any Gradute