Machine Learning Engineer

We are seeking a skilled Machine Learning Engineer with a strong Site Reliability Engineering (SRE) mindset to join our team. The ideal candidate will have hands-on experience maintaining applications on both Windows and Linux environments, managing on-premises servers, and working with Kubernetes clusters. This role requires solid Python programming skills, a good understanding of machine learning concepts, and practical knowledge of ML model deployment, monitoring, and debugging.

• 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 Data Dog 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.

• 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. Data Dog.
• 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 Dev Ops practices.
• Strong problem-solving skills and attention to detail.
• Excellent communication and collaboration skills