Requirements
Requires 5+ years of experience as a DevOps or SRE Engineer with MLOps focus. Expertise in Kubernetes, Docker, Helm, Jenkins, ArgoCD, KubeFlow, and MLFlow is essential. Proficiency in scripting (Bash, Python, PowerShell) and CI/CD principles for ML/AI is needed. Familiarity with IaC tools (Terraform, Ansible), Linux systems, and cloud platforms (AWS, Azure, GCP) is required. Strong problem-solving, communication, and collaboration skills are necessary.
Description
5+ years of hands-on experience as a DevOps Engineer, SRE Engineer, or similar role, with a focus on MLOps.Expert-level knowledge of Kubernetes, Docker, Helm, Jenkins, ArgoCD, KubeFlow, and MLFlow, with a proven track record of setting up and configuring complex MLOps deployments.Strong proficiency in scripting and automation using tools such as Bash, Python, or PowerShell.Solid understanding of CI/CD principles and best practices, specifically tailored for machine learning and AI technologies.Experience with versioning and reproducibility tools and frameworks such as Git, DVC, and MLflow.Familiarity with infrastructure-as-code (IaC) tools like Terraform and Ansible.Deep understanding of Linux systems and administration.Familiarity with cloud platforms such as AWS, Azure, or GCP, and the ability to deploy and manage MLOps infrastructure in a cloud environment. (Azure Preferred)Strong problem-solving skills and the ability to troubleshoot complex issues in a distributed systems environment.Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams. Certifications in relevant areas such as Kubernetes (CKA/CKAD), AWS, or Azure.Experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.Knowledge of model serving technologies such as TensorFlow Serving or Seldon.Experience with monitoring and logging tools like Prometheus, Grafana, ELK.