Sagemaker DevOps Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Sagemaker DevOps Engineer based in Germany.

This is an excellent opportunity 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. This position offers the chance to work with modern AWS technologies while contributing to high-impact, enterprise-level projects. It is ideal for professionals who enjoy solving complex infrastructure challenges and driving automation at scale.

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

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• 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.
  
  

Benefits

• Fully remote position within Europe.
• Flexible engagement as either a contractor or full-time employee.

Browse our curated collection of remote jobs across all categories and industries, featuring positions from top companies worldwide.

• Opportunity to work on cutting-edge AI, cloud, and machine learning projects.
• Exposure to enterprise-scale AWS and MLOps environments.
• Collaborative international team with modern engineering practices.
• Opportunities for professional growth while working with advanced cloud and DevOps technologies.
• High-impact role contributing to innovative machine learning infrastructure initiatives.
  
  

How Jobgether Works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.