Job
Description
We are seeking a highly skilled Lead DevOps Engineer with strong On-Premise infrastructure expertise to join our team and drive the end-to-end deployment, scalability, and operationalization of machine learning models in production. You will collaborate closely with data scientists, data engineers, and DevOps teams to ensure seamless CI/CD, reproducibility, monitoring, and governance of ML pipelines. Key Responsibilities Design, implement, and maintain CI/CD pipelines for deploying and monitoring microservices efficiently in on-premise environments. Manage infrastructure as code using Terraform (or equivalent on-prem solutions) for repeatable and scalable provisioning. Deploy and optimize containerized applications using Docker across on-premise environments, integrating with systems such as Harbor (or other private registries), Vault, and on-prem messaging/file storage solutions. Apply best practices for securing Docker images, including vulnerability scanning, reducing image size, and optimizing build efficiency. Implement and maintain centralized logging, monitoring, and alerting systems (e.g., Prometheus, Grafana, ELK stack) to ensure system reliability and observability. Ensure security best practices across on-prem environments, including secrets management, access control, and compliance with organizational policies. (Nice to have) Design and manage multi-client architectures within shared pipelines and storage solutions (e.g., NFS, Object Storage). 6+ years of experience in DevOps or MLOps with a strong focus on production-grade ML solutions in on-premise infrastructure. Strong expertise in CI/CD tooling, container orchestration (Docker, Kubernetes on-prem cl
