Jobs at Gcore

Build and maintain software for network management, configuration deployment, validation, testing, monitoring, visualization, and alerting. Integrate network automation into engineering deployment pipelines and automate software development, release, and operational processes. Support Linux environments and containerized networking services while researching automation technologies and improving infrastructure reliability, scalability, and efficiency.

Provide technical support for Edge Cloud products through chat, email, and phone. Diagnose networking, hardware, software, and infrastructure issues; consult customers, resolve incidents, escalate complex cases, and identify recurring problems. Document solutions, recommend preventive improvements, and share technical knowledge with the support team. The role requires Linux, networking, API, monitoring, and cloud infrastructure expertise, along with strong communication, prioritization, and customer advocacy skills.

Owns complex infrastructure projects from initiation through operational handover and closure, including data center build-outs, hardware deployments, connectivity procurement, migrations, and decommissioning. Manages schedules, budgets, risks, dependencies, vendors, logistics, installations, acceptance testing, and stakeholder communications. Coordinates architects, procurement, logistics, data center, and operations teams while maintaining Jira workflows and Kanban metrics. The role also drives process improvements and independently resolves project-level issues.

Operate and evolve globally distributed Edge Cloud infrastructure across compute, networking, virtualization, and storage. Responsibilities include Linux administration, infrastructure provisioning, lifecycle automation, maintenance, troubleshooting, reliability improvements, observability, deployment workflows, and on-call production support. The role uses GitLab CI, Ansible, Terraform, OpenStack, Python, and Bash, with opportunities to work on distributed storage, monitoring, bare-metal provisioning, and cloud service provider infrastructure.

Designs and maintains secure GitLab CI/CD pipelines, automates vulnerability scanning, hardens and signs Docker images, enforces software supply chain security policies, and secures Kubernetes workloads. The role investigates vulnerabilities, manages Linux security and system hardening, and supports compliance with open-source licensing and security standards. Responsibilities include container security, image optimization, dependency checks, Kubernetes configuration security, and automated remediation across the AI inference platform.

Provide technical support for edge network products through chat, email, and phone. Diagnose networking, hardware, software, and CDN issues; review logs, resolve complex incidents, escalate when needed, and communicate clearly with customers. Identify recurring issue patterns, recommend preventative improvements, and share technical knowledge with the support team while working rotating overnight shifts.

Lead and develop the HR Business Partner team while advising senior leaders on workforce strategy, organizational design, change management, talent planning, leadership development, employee engagement, and people risks. Establish consistent HRBP processes, coordinate company-wide initiatives, support complex employee matters, and provide direct partnership to assigned organizational units across a global technology organization.

Develop Kubernetes-native AI infrastructure using Go, including inference and training products, controllers, operators, APIs, CLIs, and developer tools. Optimize serverless container workflows, GPU scheduling, workload scaling, reliability, security, and distributed systems performance. Collaborate on cloud-native technologies and Kubernetes ecosystem advancements. Required experience includes Go, Kubernetes development, Kubernetes APIs and CRDs, Docker, Helm, and cloud-native architecture; Python, AI/ML frameworks, GPU optimization, and open-source contributions are preferred.

Build and optimize Gcore’s AI inference platform using Python, PyTorch, inference frameworks, GPUs, and Kubernetes. Deploy language and multimodal models, improve latency, throughput, memory efficiency, GPU utilization, reliability, and cost, and debug issues across software, infrastructure, hardware, networking, and distributed systems. Collaborate with platform, infrastructure, product, and customer-facing teams, while contributing to open-source inference projects.

Build and operate Python services and APIs for GPU virtual machines and bare-metal infrastructure. Develop provisioning, placement, maintenance, recovery, capacity, and scheduling workflows; integrate GPU servers with OpenStack and Kubernetes; automate validation and production readiness; and improve testing, observability, deployment safety, and operational tooling. Investigate complex production issues across compute, networking, storage, virtualization, and hardware while owning projects through rollout and ongoing operation.