Site Reliability Engineer

Description

• Collaborate with ML engineers and data scientists to operationalize models, ensuring scalability, reliability, and adherence to established MLOps procedures and best practices.
• Assist in the deployment and maintenance of machine learning models in production environments under direct supervision, learning containerization technologies like Docker and Kubernetes.
• Contribute to infrastructure automation and configuration management for ML systems, learning Infrastructure as Code (IaC) practices with tools like Terraform or CloudFormation.
• Support CI/CD pipeline development for ML workflows, including model versioning, automated testing, and deployment processes using tools like Azure DevOps.
• Monitor ML model performance, data drift, and system health in production environments, implementing basic alerting and logging solutions.

Requirements

• Understanding of Infrastructure as Code (IaC) tools like Terraform or Ansible.
• Proficiency with Python or other scripting languages (Shell / Bash / PowerShell / Perl) for automation scripting and system integration.
• 2 to 5 years of professional experience in in DevOps, MLOps, or systems engineering environment.
• Basic understanding of security best practices for ML systems and data governance.
• Familiarity with version control systems (Git) and collaborative development workflows.
• Familiarity with MLOps tools and frameworks (MLflow, Kubeflow, DVC, or similar).
• Foundational knowledge of DevOps principles and practices, with understanding of CI/CD concepts and basic system administration.
• Basic understanding of machine learning concepts and the ML model lifecycle from development to production.
• Exposure to model serving frameworks (TensorFlow Serving, TorchServe, ONNX Runtime).
• Basic experience with monitoring and observability tools (Prometheus, Grafana, ELK stack).
• Experience with Microsoft Azure and its services including ML/AI (Azure ML, Azure DevOps, etc.) – Must Have
• Understanding of containerization technologies (Docker) and basic orchestration concepts (Kubernetes fundamentals).
• Experience with Windows/Linux system administration and command-line tools.
• Knowledge of database systems and data pipeline technologies.
• Bachelor's degree in Computer Science, Engineering, Information Technology, or a closely related technical field. Trimble's Professional ladder typically requires four or more years of formal education.

Benefits

Training + Development

Interview process

Visa Sponsorship

Security clearance