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Gcore operates distributed edge infrastructure and cloud services for media delivery, now pivoting toward AI inference workloads. The tech stack—Kubernetes, TensorFlow, PyTorch, vLLM, and OpenStack—combined with active projects in AI inference infrastructure and Kubernetes-native AI systems signal a strategic shift from pure CDN/hosting toward inference-as-a-service. Senior-heavy hiring (43 of 78 roles) concentrated in engineering reflects the complexity of building multi-region AI compute, not incremental scaling.
Tech Stack 75 technologies
Core StackKubernetes Docker Python Go Helm TensorFlow PyTorch Linux PostgreSQL Puppet Ansible Terraform Prometheus Grafana Podman FastAPI RabbitMQ Intel NVIDIA Dell Equinix vLLM SGLang Cisco ASA Cisco Firepower BGP Google Sheets Excel OpenStack Asyncio+43 more
What Gcore Is Building
◆Challenges
- Scalable ai inference
- Performance reliability cost security
- Ddos mitigation and high availability
- Bottlenecks and single points of failure
- Scaling ai inference workloads
- Network efficiency and latency reduction
- Capacity expansion and pop openings
- Reducing latency
- Legacy data cleanup
- Forecasting accuracy
▲Active Projects
- Ai inference and training infrastructure
- Monitoring and observability for ai inference platforms
- Kubernetes-native ai infrastructure
- Mvps and proof-of-concept solutions for non-standard customer onboarding scenarios
- Ai inference and model training cloud products
- Network automation and monitoring implementation
- Event tracking design
- Self-service optimization
- System architecture for ai workloads
- Real-time visibility apis
Hiring Activity
80 roles · 40 in 30d
Department
Seniority
Company intelligence
