ML Ops Engineer - Lead

Key Responsibilities

• Design, build, and maintain scalable ML pipelines in on-premises environments, ensuring high availability and reliability.
• Implement and manage Infrastructure as Code (IaC) using tools like Ansible, Terraform (for private cloud), or Puppet for development, testing, and production setups.
• Extend and enhance existing ML workflows to support evolving data science needs with minimal but impactful changes.
• Act as the infrastructure and MLOps SME, collaborating with data scientists to guide and support model deployment and operationalization.
• Document system architecture, infrastructure usage, and design via tools like Confluence, GitHub Wikis, and architectural diagrams.
• Research and implement optimizations for ML workflows, compute resource utilization, and storage management.
• Lead cross-functional initiatives related to ML product deployment, re-platforming, and modernization efforts in the on-prem environment.