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 (SRE) mindset to join their team. The candidate will be responsible for maintaining and supporting machine learning applications, managing Kubernetes clusters, and collaborating with data scientists to ensure reliable deployment of ML 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
Company Overview