Description

You will build and maintain the infrastructure required to deploy, monitor, and manage machine learning models efficiently.

Responsibilities

• Implement, optimize, and maintain CI/CD pipelines for ML systems using GitHub workflows and Jenkins.
• Partner with data scientists and platform teams to integrate ML models into core evaluation platforms.
• Administer ML development and production environments using cloud-native solutions, optimizing for scalability and cost.
• Evaluate and deploy automation tools to streamline the end-to-end ML lifecycle.
• Enhance quality evaluation features and ensure monitoring via dashboards and automated alerts.

Required Skills

• Expert proficiency in Python.
• Minimum 5 years in software engineering, including 2+ years in DevOps/MLOps in cloud/production environments.
• Deep experience with Kubernetes and Infrastructure as Code (Terraform, CloudFormation).
• Strong working knowledge of AWS services (S3, Lambda, DynamoDB).
• Proficiency with containerization technologies like Docker.
• Experience with CI/CD systems such as Jenkins and GitLab.
• Familiarity with ML pipeline tools like Kubeflow, Airflow, or MLflow.
• Working knowledge of Typescript and Shell script languages.

Key Skills

Education

Master's degree