Jobs at Gcore

Build and maintain Python backend features for Gcore’s billing engine, improve application performance and reliability, develop REST APIs, contribute to distributed architecture decisions, and enhance CI/CD and delivery processes. The role requires collaboration with a globally distributed team and involves technologies including Django, PostgreSQL, RabbitMQ, Celery, Kubernetes, and observability tooling.

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.

Deploy and maintain cloud infrastructure, implement infrastructure changes, troubleshoot software and hardware issues, and improve infrastructure lifecycle pipelines. The role requires Linux, OpenStack, configuration management, networking, scripting, virtualization, monitoring, and on-call participation. Ceph experience is preferred.

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.

Provide technical support for Edge Cloud customers and internal users through chat, email, and phone. Diagnose networking, hardware, software, Linux, API, and infrastructure issues; resolve incidents or escalate them appropriately. Analyze recurring issues, recommend preventive improvements, document solutions, and share technical knowledge. The role requires customer advocacy, clear communication, prioritization, and a rotating schedule of two 12-hour day shifts followed by two days off.

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.

Owns infrastructure deployment and change projects from intake through closure, including planning, scheduling, budget estimation, risk management, procurement coordination, logistics, data center installation, operational handover, acceptance testing, and stakeholder communication. Projects include hardware deployments, capacity expansions, connectivity procurement, colocation, site migrations, and point-of-presence build-outs. The role uses Jira and Kanban flow metrics to manage dependencies, timelines, budgets, and project reporting.

Designs, develops, and maintains automated tests for backend services and APIs. Builds scalable testing frameworks, internal QA tools, and CI/CD integrations; analyzes failures and performs root-cause analysis. Collaborates with development, DevOps, and product teams to improve coverage, reliability, and execution efficiency. Contributes to quality strategy, automation best practices, and AI-assisted testing workflows.

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 maintain software for network configuration, monitoring, validation, testing, and automation. Integrate network automation into engineering deployment pipelines and automate software development, release, and operations. Support Linux environments and containerized network services, monitor infrastructure health, research automation technologies, and improve the reliability and scalability of mission-critical systems.

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.

Design, build, and maintain a Go-based Traffic Management System API and edge agent to render/apply routing configuration. Implement routing (BGP/Anycast), automated failover, high-availability features, and strengthen reliability via tests, metrics, and observability while collaborating with cross-functional teams.

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.

Design, deploy, and maintain on-premises infrastructure for scalable AI inference workloads. Build GPU scheduling and model deployment pipelines, manage monitoring and observability systems, troubleshoot Kubernetes, Linux, networking, and production infrastructure, and collaborate with ML and platform teams on architecture and performance testing.