Software Python Engineer (GPU Cloud)

Gcore is a global provider of infrastructure and software solutions for AI, cloud, network, and security with over 210 edge locations, 50 cloud regions, and thousands of GPUs. The company collaborates with technology partners including Intel, NVIDIA, Dell, and Equinix, supporting digital products globally. They have a team of over 550 professionals and focus on designing infrastructure for an AI-driven world.

• 5+ years of experience writing reliable, well-tested production code.
• Strong Python skills and experience building APIs, backend services, or distributed systems.
• Hands-on experience with OpenStack or another cloud control plane.
• Good understanding of Linux, networking, and compute virtualization.
• Practical experience with Kubernetes and containerized workloads.
• Experience owning production services where reliability and scale are important.
• Ability to debug complex problems across multiple layers of a system.
• Good communication skills and a collaborative approach to engineering.

Nice to have:

• Experience with KVM, QEMU, libvirt, PCI passthrough, vGPU, or MIG.
• Experience with NVIDIA drivers, CUDA, NVML, DCGM, GPU Operator, or Kubernetes device plugins.
• Experience with bare-metal provisioning, BMC, Redfish, firmware, or hardware validation.
• Experience with Ceph, InfiniBand, RDMA, RoCE, or GPUDirect.
• Experience with Terraform, Ansible, Helm, GitOps, or CI/CD tooling.
• Experience with Golang or another systems programming language.
• Contributions to OpenStack, Kubernetes, Linux, or other open-source infrastructure projects.

• Build Python services and APIs for the lifecycle of GPU virtual machines and bare-metal servers.
• Develop workflows for provisioning, placement, maintenance, quarantine, and recovery.
• Improve capacity tracking, scheduling, and availability to customers.
• Integrate new GPU servers and configurations into OpenStack and connect them with Gcore's Kubernetes services.
• Automate host validation, image preparation, driver installation, and production readiness checks.
• Improve testing, observability, deployment safety, and operational tooling.
• Investigate production issues across the control plane, hypervisors, networking, storage, and hardware.
• Work closely with compute, Kubernetes, networking, SRE, hardware, and product teams.
• Own projects from design and implementation through rollout and production operation.