AI Domain Solution Architect

Key Responsibilities

• Drive Technical Architecture & Solution Design: Lead discovery sessions, technical evaluations, architecture workshops, and infrastructure sizing for complex customer AI environments—translating enterprise workloads such as LLM inference, fine-tuning, and RAG into scalable architectures built on SUSE AI Factory, Rancher Prime, and the NVIDIA accelerated computing stack.
• Deliver Technical Validation & Field Sales Execution: Partner with Account Executives and field Solution Architects throughout the sales cycle to lead technical qualification, build targeted PoCs, deliver hands-on demonstrations, and develop technical proposals and RFI/RFP responses.
• Guide Agentic AI Architecture: Help customers understand the infrastructure, integration, security, and governance requirements for agentic AI, multi-agent systems, and Model Context Protocol (MCP) integrations in enterprise production environments.
• Collaborate Across the AI Ecosystem: Jointly architect customer solutions with strategic ecosystem partners—including hardware vendors, systems integrators, and cloud providers—to optimize AI compute, high-speed networking, storage, and overall platform architecture.
• Build Field Capability & Repeatable Assets: Develop reusable reference architectures, solution blueprints, demonstrations, and technical enablement materials that scale AI expertise across regional and global field teams.
• Act as the Voice of the Customer: Capture technical feedback and emerging platform requirements from customer environments and communicate them to SUSE Product Management and Engineering to help inform product direction.
• Serve as an AI Infrastructure SME: Translate complex topics—including GPU architecture and partitioning, workload scheduling, multi-tenancy, high-performance networking, storage, and air-gapped operations—into clear technical and business value for audiences ranging from platform engineers to C-suite technology executives.

Skills & Experience

• Presales Architecture Experience: Proven track record translating deep technical expertise into effective customer solutions, proposals, presentations, demonstrations, RFP responses, and hands-on workshops in a presales, solution architecture, or technical consulting role.
• Technical Communication & Leadership: Ability to simplify complex AI and infrastructure concepts for both technical and executive audiences, connecting technical architecture to business outcomes. Demonstrated ability to coordinate cross-functional teams, influence stakeholders in matrixed environments, and drive execution with a team-first mindset.
• AI Infrastructure & Accelerated Computing: Hands-on experience designing, operating, and sizing GPU-accelerated infrastructure for AI training, fine-tuning, and inference workloads. Practical understanding of GPU memory and compute characteristics, resource partitioning, workload scheduling, utilization, and multi-tenancy, together with familiarity with technologies such as vLLM, NIM, RAG, and vector databases.
• NVIDIA Ecosystem Proficiency: Working knowledge of the NVIDIA AI and accelerated computing ecosystem, including GPU architecture, NVIDIA AI Enterprise, CUDA, GPU and Network Operators, NCCL, GPUDirect, MIG, Run:ai, and high-performance interconnects.
• Container Management & Cloud-Native Platforms: Expert-level Kubernetes architecture and cluster administration experience, with RKE2, K3s, and Rancher Prime preferred. Deep proficiency with Helm, GitOps, CNI, container runtimes, observability, workload scheduling, and multi-cluster lifecycle operations.
• Linux Systems Expertise: Strong Linux systems knowledge, including networking, storage, containers, security, troubleshooting, and performance analysis in production environments.
• SUSE Platform Knowledge: Familiarity with, or the ability to rapidly acquire expertise in, SUSE AI Factory, SUSE Rancher Prime, SUSE Linux Enterprise Server (SLES), SUSE Security (NeuVector), and SUSE Observability.
• High-Performance Networking & Storage: Solid understanding of high-bandwidth, low-latency networking—including Ethernet, RoCEv2, RDMA, and InfiniBand—and distributed and object storage architectures supporting high-performance AI workloads.
• Enterprise Security & Operations: Experience designing secure production architectures, including container security, software supply-chain security, identity and access management, workload isolation, air-gapped/disconnected environments, and enterprise AI governance requirements.

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