## Sagemaker DevOps Engineer
We are looking for an experienced DevOps professional to help build and optimize enterprise-scale machine learning infrastructure in a fully remote environment. In this role, you will design, automate, and maintain cloud-based MLOps solutions that enable seamless model development, deployment, and operations. Working at the intersection of DevOps and machine learning, you will create scalable platforms, improve development workflows, and enhance operational efficiency across AI initiatives. You will collaborate with cross-functional engineering teams to deliver reliable, secure, and automated cloud environments.
### Accountabilities
- Design, build, and automate enterprise-grade AWS SageMaker environments to support scalable machine learning initiatives
- Develop and implement DevOps automation for SageMaker Unified Studio and related cloud infrastructure
- Configure and maintain SageMaker lifecycle configurations to improve development consistency and operational efficiency
- Build and optimize CI/CD pipelines that enable users to deploy custom Docker images, kernels, and machine learning workloads
- Develop monitoring, alerting, and cost-control mechanisms to ensure platform reliability, service availability, and efficient resource utilization
- Implement MLOps automation for model deployment and infrastructure promotion across multiple environments
- Collaborate with engineering and platform teams to continuously improve cloud architecture, deployment processes, and operational best practices
### Requirements
- 6+ years of professional experience in DevOps, Cloud Engineering, Infrastructure Engineering, or a related technical field
- Expert-level experience with AWS services and Python development
- Strong hands-on experience with Amazon SageMaker and machine learning infrastructure
- Proven experience designing and implementing enterprise-scale DevOps automation solutions
- Solid understanding of CI/CD principles and infrastructure automation
- Experience building Jenkins pipelines is considered an advantage
- Experience implementing MLOps workflows and automated model deployment processes is preferred
- Strong analytical and troubleshooting skills with the ability to work independently in remote, distributed teams
- Excellent communication skills and a proactive, solution-oriented approach to problem solving