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.