Engineer - AI

1. MlOps JD: Key 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 ing 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. Required Qualifications: 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, with a solid background in developing Python-based solutions and applications. Deep understanding of machine learning workflows, including practical knowledge of ML model training processes, frameworks, and evaluation. Proven experience in ML deployment development, including building out end-to-end model serving pipelines and managing the model lifecycle. 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.