Description

Own the reliability, scalability, and observability of AI/ML infrastructure across cloud environments.

This role is remote.

Responsibilities

• Manage production on-call rotations, lead incident command, and conduct blameless postmortems.
• Design and maintain cloud-native infrastructure using Kubernetes, Docker, and Terraform/OpenTofu on AWS, GCP, or Azure.
• Implement comprehensive observability using Prometheus, Grafana, and distributed tracing for metrics, logging, and alerting.
• Automate operational tasks and CI/CD pipelines using Python, Bash, or Go with tools like GitHub Actions, GitLab CI, Jenkins, or ArgoCD.
• Troubleshoot complex distributed systems, including networking fundamentals and cross-cloud connectivity.

Required Skills

• 10+ years of experience in Site Reliability Engineering or infrastructure roles.
• Production Kubernetes and container experience (Docker) with cloud-native patterns.
• Hands-on ownership of at least one major cloud provider: AWS, Google Cloud Platform, or Azure.
• Proficiency in infrastructure as code using Terraform or OpenTofu.
• Strong automation skills in Python, Bash, or Go.
• Deep observability experience with Prometheus, Grafana, or equivalent tools for metrics, logging, and alerting.
• Understanding of networking fundamentals: VPCs, load balancers, DNS, firewalls.
• Experience with CI/CD systems such as GitHub Actions, GitLab CI, Jenkins, or ArgoCD.
• Proven ability to troubleshoot complex distributed systems largely self-directed.

Preferred Skills

• Experience with GPU infrastructure and AI/ML workloads (Ray, Kubeflow, MLflow) including NVIDIA GPU orchestration (A100/H100, CUDA).
• Knowledge of distributed training networking (RDMA, InfiniBand, EFA, NCCL) and distributed tracing (OpenTelemetry).
• Familiarity with progressive delivery (canary/blue-green), chaos engineering, and FinOps/cost optimization.

Key Skills

Education

Any Graduate