Description

Deploy internal and open-source ML frameworks, models, and pipelines into production Kubeflow environments.

Responsibilities

• Deploy and manage ML frameworks and pipelines in production Kubeflow.
• Create and manage production-scale Kubernetes clusters.
• Write Infrastructure-as-Code using Terraform or CloudFormation.
• Resolve critical and complex technical issues within Kubernetes-based projects.
• Maintain docker-based microservices APIs.

Required Skills

• 5+ years of industry experience in Software Engineering, DevOps, or Data Engineering.
• 2+ years of experience with Kubernetes and Docker.
• Deep understanding of Kubernetes networking and core concepts including Deployment, ReplicaSet, DaemonSet, Statefulsets, Jobs, Secrets, Ingress, and Storage services.
• Proficiency in Python and scripting languages.
• Experience with workflow management tools such as Airflow, Nextflow, or Argo.
• Experience building CI/CD pipelines for ML applications using GitLab or Jenkins.
• Hands-on experience with AWS, Azure, or GCP cloud environments.
• Knowledge of networking fundamentals.

Key Skills

Education

ANY GRADUATE