Staff Network Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Network Engineer (AI Fabric, Datacenter and Edge Networking) - Radian Arc based in Portugal.
This is a high-impact Staff-level networking role responsible for the architecture, deployment, and operation of infrastructure powering GPU-based AI workloads.
You will own the technical direction of high-performance AI fabrics while remaining deeply hands-on across implementation, troubleshooting, automation, and operations.
The scope spans RoCE/RDMA GPU fabrics, datacenter routing, edge connectivity, security, and inter-datacenter backbone infrastructure.
As the organization's first dedicated networking specialist, you will establish reusable architectures, engineering standards, and operational practices from the ground up.
You will work closely with compute, storage, platform, SRE, observability, and datacenter teams to make networking a core part of the infrastructure design.
The role combines architectural leadership with direct execution in a lean, fast-scaling, international environment.
It is an opportunity to shape the networking foundation for distributed AI training and inference across global datacenter and edge deployments.
Accountabilities
AI fabric architecture:
Design, deploy, and operate high-performance GPU networking fabrics for distributed AI workloads, including RoCE, RDMA, Spectrum-X, leaf-spine, fat-tree, rail, and multi-plane architectures.
AI performance optimization:
Analyze GPU communication patterns and optimize east-west traffic, congestion control, latency, throughput, and reliability for distributed training and inference workloads.
Datacenter networking:
Own Layer 2/3 architecture using BGP, ECMP, EVPN/VXLAN, VLAN/VRF, OVS/OVN, Linux networking, bridges, overlays, and scalable routing patterns.
Security and edge connectivity:
Design and operate north-south connectivity, WAF and application-layer protections, TLS termination, DDoS mitigation, and gateway infrastructure.
Inter-datacenter networking:
Design private connectivity, dark-fiber and metro-fiber rings, high-capacity WAN links, DWDM transport, inter-site BGP routing, redundancy, and failure-domain isolation.
End-to-end delivery:
Lead infrastructure initiatives from architecture and lab validation through BOM validation, datacenter layouts, deployment, acceptance, and production rollout.
Operational excellence:
Establish automation for provisioning, configuration management, monitoring, lifecycle management, validation, and day-2 operations while improving reliability and recovery practices.
Incident leadership:
Act as the senior escalation point for complex network incidents, leading investigation, root-cause analysis, remediation, and long-term architectural improvements.
Performance and reliability:
Define and monitor SLAs, SLOs, latency, reliability, capacity, and operational metrics, translating findings into concrete engineering improvements.
Cross-functional leadership:
Partner with infrastructure, platform, SRE, compute, storage, observability, and datacenter teams while setting networking standards and influencing long-term architecture.
Technical leadership:
Serve as the primary networking design authority, mentor engineers in adjacent domains, and establish reusable patterns that can scale with the organization.
Automation and leverage:
Build Python and/or Bash tooling, validation frameworks, and operational systems that reduce manual work and increase consistency across deployments.
Requirements
Network engineering experience:
Strong hands-on experience designing and operating large-scale datacenter networks in production, with expert knowledge of BGP, OSPF, ECMP, and EVPN/VXLAN.
AI networking expertise:
Deep experience designing and operating high-performance GPU networking fabrics for distributed AI workloads, including practical RoCE/RDMA expertise.
GPU communication knowledge:
Strong understanding of NCCL communication patterns—including all-reduce, all-gather, broadcast, and reduce-scatter—and their implications for network topology and performance.
RoCE optimization:
Experience tuning large-scale RoCE fabrics, including PFC and ECN congestion management, and diagnosing NCCL stalls, RDMA congestion, fabric hotspots, and packet-loss issues.
Production infrastructure:
Proven experience with high-speed Ethernet and NVIDIA/Mellanox networking platforms, as well as high-performance interconnects, optics, and networking hardware.
Systems troubleshooting:
Ability to investigate complex cross-layer problems spanning hardware, firmware, Linux/kernel networking, and distributed application communication.
Automation:
Strong Python and/or Bash skills, with experience applying software engineering pract