SRE

OpenKyber LLC
Atlanta, GA, United States
3 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Compensation
$145,600.0
Working hours
Regular working hours

Tech stack

OpenTofu Artificial Intelligence Amazon Web Services Microsoft Azure Bash Shell Nvidia CUDA Continuous Integration Disaster Recovery Distributed Systems Domain Name System (DNS) Github Python (Programming Language)
+17 more
Networking Basics Prometheus Data Logging Google Cloud Load Balancing Cloud Platform System Grafana Multi-Cloud Firewalls (Computer Science) Gitlab-ci Kubernetes Machine Learning Operations Hardware Infrastructure Terraform Dynatrace Docker Jenkins

Job description

  • Distributed tracing and Open Telemetry instrumentation across services.
  • Progressive delivery: canary and blue/green rollouts with automated rollback.
  • Chaos or fault-injection testing, game days, and disaster-recovery drills.
  • Multi-cloud networking, unified storage abstractions, and disaster recovery.
  • FinOps and cost optimization: Spot, Reserved Instances, Savings Plans.
  • Establishing an SRE function where one did not previously exist.

For applications and inquiries, contact:hirings@openkyber.com

Requirements

Production on-call experience in a real rotation, with incident command and blameless postmortem practice. Production Kubernetes and container experience (Docker), with cloud-native infrastructure patterns. Hands-on production ownership on at least one major cloud (AWS, Google Cloud Platform, or Azure). Terraform or OpenTofu proficiency. Observability depth with Prometheus, Grafana, or equivalent for metrics, logging, and alerting, including dashboard and alert design. Strong automation skills in Python, Bash, or Go. Networking fundamentals: VPCs, load balancers, DNS, firewalls, cross-cloud connectivity. CI/CD experience with GitHub Actions, GitLab CI, Jenkins, or ArgoCD. Proven ability to troubleshoot complex distributed systems, largely self-directed. Preferred Qualifications

  • GPU infrastructure and AI/ML workloads: Ray, Kubeflow, ML flow, or similar.
  • NVIDIA GPU orchestration: A100/H100 configuration, driver and CUDA runtime management.

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