Posted:2 weeks ago|
Platform:
Hybrid
Full Time
JD (needs bifurcation):
DevOps Engineer with expertise in Python and Kubernetes to design and manage scalable infrastructure using tools like KEDA, Grafana, and Karpenter. The role focuses on GPU-accelerated workloads and CI/CD pipelines, with a nice-to-have knowledge of AI/ML deployment.
2 Positions
Key Responsibilities
Deploy and manage Kubernetes clusters, using Karpenter for node autoscaling.
Implement event-driven scaling with KEDA for dynamic workloads.
Build Grafana dashboards for monitoring with Prometheus integration.
Optimize GPU resources in Kubernetes for compute-intensive tasks.
Develop Python scripts for automation, IaC, and CI/CD pipelines.
Manage containerized deployments with Docker and Helm.
Troubleshoot production issues to ensure high availability.
Collaborate on CI/CD workflows using Jenkins, GitLab.
Mentor junior engineers and enforce DevOps best practices.
Good-to-Have Skills
Understanding of AI architecture (neural networks, inference).
Golang proficiency for high-performance tools.
Experience with ML deployment (Kubeflow, TensorFlow Serving).
Familiarity with ML model development (scikit-learn, PyTorch).
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