Note: The job is a remote job and is open to candidates in USA. Dice is seeking an experienced Machine Learning Engineer with a strong Site Reliability Engineering mindset to join their team. The role involves maintaining applications in Windows and Linux environments, managing on-premises servers, and working with Kubernetes clusters to ensure reliable deployment and monitoring of machine learning models.

Responsibilities

• Maintain and support machine learning applications running on Windows and Linux servers in on-premises environments
• Manage and troubleshoot Kubernetes clusters hosting ML workloads
• Collaborate with data scientists and engineers to deploy machine learning models reliably and efficiently
• Implement and maintain monitoring and alerting solutions using DataDog to ensure system health and performance
• Debug and resolve issues in production environments using Python and monitoring tools
• Automate operational tasks to improve system reliability and scalability
• Ensure best practices in security, performance, and availability for ML applications
• Document system architecture, deployment processes, and troubleshooting guides

Skills

• Proven experience working with Windows and Linux operating systems in production environments
• Hands-on experience managing on-premises servers and Kubernetes clusters and Docker containers
• Strong proficiency in Python programming
• Solid understanding of machine learning concepts and workflows
• Experience with machine learning model deployment and lifecycle management
• Familiarity with monitoring and debugging tools, e.g. DataDog
• Ability to troubleshoot complex issues in distributed systems
• Experience with CI/CD pipelines for ML applications
• Familiarity with AWS cloud platforms
• Background in Site Reliability Engineering or DevOps practices
• Strong problem-solving skills and attention to detail
• Excellent communication and collaboration skills
• We need an engineer who is also familiar with model development

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