DevOps Engineer (AI Inference)

Gcore is a global provider of infrastructure and software solutions for AI, cloud, network, and security. The company operates 210+ edge locations, 50+ cloud regions, and thousands of GPUs. Gcore collaborates with technology partners such as Intel, NVIDIA, Dell, and Equinix. They serve a global user base with platforms powering digital products worldwide and have a team of over 550 professionals.

• Strong understanding of Kubernetes architecture, including CNI, CSI, operators, ingress/gateway, and control plane components.
• Hands-on experience operating and troubleshooting production Kubernetes clusters.
• Strong Linux and networking troubleshooting skills, including DNS, routing, firewalling, TLS, MTU, connectivity, and performance issues.
• Ability to develop automation and operational tooling using Python, Go, or Bash.
• Experience with Terraform, Ansible, or similar IaC/configuration management tools.
• Experience with VictoriaMetrics/Grafana or similar monitoring, alerting, and troubleshooting tools.
• Strong experience with Git-based workflows and CI/CD pipelines.

Preferred Qualifications:

• Familiarity with Cluster API or similar Kubernetes cluster lifecycle management technologies.
• Hands-on operation or administration of Slurm clusters.
• Knowledge of Argo CD, GitOps workflows, Helm, or Helmfile.
• Background working with managed platforms, PaaS, or cloud services.
• Exposure to bare metal, GPU, HPC, or other high-performance computing environments.

Nice to have:

• Familiarity with the NVIDIA GPU stack, RDMA/InfiniBand, or high-performance networking.
• Knowledge of OpenStack or similar cloud infrastructure platforms.
• Hands-on experience developing Kubernetes operators or controllers.

• Design, develop, and maintain infrastructure for AI inference workloads, including GPU scheduling, model deployment pipelines, and data access patterns in on-premises environments.
• Build and manage monitoring and observability tools for AI inference platforms, including dashboards, alerts, and runbooks for model health and system performance.
• Collaborate with ML engineers and platform teams to design system architecture for AI workloads, integrate inference runtimes, and test performance at scale.

We provide equal opportunity to all applicants without regard to legally protected characteristics. Benefits may vary depending on your location.