Senior Solutions Architect – AI Infrastructure Networking

About the role We are seeking a Senior Solutions Architect with deep networking expertise to join the Voyager team in the Mirantis Office of the CTO. You will own the network solutioning of k0rdent AI, our platform for building and operating GPU clouds and AI factories, from metal to model. You will design how large GPU clusters are interconnected, how tenants are isolated, and how workloads reach the network from containers, virtual machines and bare metal nodes. You will turn that design into published reference architectures, working code and proofs of concept that run on real hardware with our customers and partners. As part of the Office of the CTO, research is a core part of the job. You will explore designs and technologies before customers ask for them, prototype ideas that may not ship, and challenge established practice when a better approach exists. Your work will shape where k0rdent AI goes next, not only how it is deployed today. This role combines research with hands-on, outward-facing work. You will split your time between exploring new designs, writing architecture, building and validating it in the lab, and explaining it to engineers, executives and conference audiences. You will work with Solutions Architects, Partner Management and Engineering colleagues across many countries and time zones. Key Responsibilities Reference architecture • Design and publish network reference architectures and solution designs for k0rdent AI, covering data center topologies from a single rack to multi-thousand-GPU clusters. • Define compute (east-west) fabrics on InfiniBand and on RoCEv2 Ethernet, including rail-optimised and fat-tree / Clos designs, oversubscription and failure-domain choices. • Define front-end, storage and management networks, and how they connect to customer data centers, public clouds and hybrid environments. • Specify multi-tenant isolation across the stack: InfiniBand partitions (PKeys), VRFs and EVPN-VXLAN on Ethernet, and Kubernetes-level network policy. • Document scale limits and trade-offs (cost, performance, operability, vendor lock-in) with defensible reasoning a customer can follow.   Host and platform networking • Define how Linux hosts expose NICs, DPUs and SuperNICs to workloads: PCI passthrough, SR-IOV virtual functions, IOMMU groups, NUMA and GPU-NIC affinity. • Design Kubernetes networking for AI workloads: primary and secondary CNIs, Multus, SR-IOV and RDMA device plugins, NVIDIA Network Operator, and Dynamic Resource Allocation (DRA). • Design networking for virtual machines on KubeVirt, including passthrough and SR-IOV for GPU and RDMA traffic. Research and exploration • Track and evaluate emerging technologies and standards in AI networking, such as Ultra Ethernet (UEC), SuperNIC and DPU offloads, scale-up and scale-across fabrics, and Kubernetes DRA network drivers. • Prototype alternative designs in the lab, including ones customers have not yet asked for, and measure them against current practice. • Challenge established or customer-preferred designs when evidence points to a better option, and make the case with data. • Publish findings as internal research notes and design proposals and, where appropriate, as external papers, blog posts or talks. Code and proofs of concept • Build and run proofs of concept with customers and partners, on Mirantis lab hardware and on customer sites, and report results against agreed success criteria. • Write automation and tooling (Python, Go, Bash, Ansible, Helm, Kubernetes manifests, Terraform) that makes the reference architectures reproducible. • Run validation and benchmarking of fabrics and host configurations (e.g. NCCL tests, perftest, ib_write_bw) and turn findings into design guidance. Customers, partners and community • Ac