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.