ML Ops Engineer

Responsibilities:

• Design and implement data pipelines and engineering infrastructure to support enterprise machine learning systems at scale.
• Work closely with data scientists and engineering teams to deploy, monitor, and optimize machine learning models in production.
• Identify, evaluate, and integrate new technologies to enhance performance, maintainability, and reliability of machine learning solutions.
• Apply software engineering best practices to machine learning pipelines, including CI/CD, automation, monitoring, and version control.
• Manage cloud infrastructure (AWS, Azure, GCP) and containerization (Docker, Kubernetes) to ensure scalable and efficient ML workloads.
• Implement and maintain highly available and scalable machine learning environments.
• Ensure the security and compliance of machine learning systems, adhering to governance and industry regulations.
• Troubleshoot and optimize machine learning models and infrastructure for performance improvements.
• Collaborate with IT and OT teams to ensure seamless integration of machine learning systems.
• Use Infrastructure as Code (Terraform, CloudFormation) to automate the management and provisioning of infrastructure.
• Implement automated processes for deployment, monitoring, logging, and performance tracking.