MLOps Engineer II (Remote)

About the Role

As MLOps Engineer II, you will focus on supporting cross-functional teams in designing, deploying, and operating machine learning solutions while building scalable infrastructure, tools, and best practices across the Machine Learning Engineering (MLE) ecosystem.

What You’ll Do

• Collaborate with Data Scientists and Engineers across the full ML lifecycle, including building and scaling ETL pipelines, deploying models into customer-facing applications, and enabling efficient model development through cloud infrastructure and tooling
• Design, build, and maintain scalable machine learning infrastructure, including model serving (real-time and batch), training environments, and orchestration systems, with a focus on performance, scalability, and cost efficiency
• Contribute to the roadmap for Machine Learning Engineering and Data Science tools, including developing reusable frameworks and standardized solutions to streamline model implementation
• Partner with and support Data Scientists by enabling effective use of cloud-based tools and infrastructure, and providing technical expertise across the ML lifecycle
• Collaborate with machine learning engineers to share knowledge, improve best practices, and foster a culture of continuous learning and development
• Support development and maintain monitoring, alerting, and automated testing frameworks to ensure the reliability, performance, and integrity of data pipelines, models, and infrastructure
• Develop, document, and communicate implementations and best practices across the data science lifecycle
• Manage and communicate cloud infrastructure costs and budgets to project stakeholders
• Stay current with GCP services and evolving best practices in Machine Learning Engineering and MLOps
• Additional tasks may be assigned

What Skills You Have

Required

* Experience in MLOps or DevOps practices, including building and operating production ML systems using Docker, Kubernetes, CI/CD pi...