Senior Solutions Architect (VMware & AI Platform)

Location and Language

• The role is based in Riyadh, Saudi Arabia, and is explicitly on-site.
• Fluent Arabic and English are required.

Role Overview

Core Responsibilities

• Build trusted relationships with customer executives and technical stakeholders.
• Lead workshops on VMware, private cloud, datacentre modernization, hybrid cloud, AI platforms, and transformation.
• Design VMware solutions using VCF, vSphere, NSX, Aria, Tanzu, and related technologies.
• Design GPUaaS, model serving, inference, fine-tuning, and Kubernetes-orchestrated AI platforms.
• Architect MLOps tooling, model registries, GPU scheduling, multi-tenancy, and model CI/CD pipelines.
• Embed AI guardrails, governance, data protection, prompt safety, and regulatory alignment into designs.
• Design high availability, disaster recovery, GPU failover, checkpointing, and continuity for stateful services.
• Design platform security through micro-segmentation, confidential computing, encryption, secure supply chains, and access controls.
• Assess requirements and develop architectures for traditional infrastructure and AI workloads.
• Lead presales engagements and coordinate with SMEs, product, sales, and delivery teams.
• Prepare proposals, RFI/RFP responses, bids, Statements of Work, estimates, and delivery approaches.
• Maintain Salesforce records, provide advisory guidance, mentor colleagues, and contribute technical leadership.

Required Qualifications

• A relevant bachelor's degree or alternatively 10+ years of relevant technical presales, consulting, or solution architecture experience.
• Extensive enterprise infrastructure and VMware solution design experience.
• Strong understanding of virtualization, workload migration, consolidation, private cloud, hybrid cloud, and infrastructure modernization.
• Expertise with VMware Cloud Foundation, vSphere, vCenter, NSX, Aria Operations and Automation, and Tanzu.
• Practical knowledge of GPUaaS, GPU virtualization and partitioning, multi-tenancy, and NVIDIA or AMD accelerators.
• Working knowledge of model serving, inference optimization, fine-tuning, training workflows, and MLOps tooling.
• Strong Kubernetes and container platform knowledge, particularly VMware Tanzu or TKG.
• Knowledge of AI guardrails, data protection, responsible AI, ISO/IEC 42001, the EU AI Act, or NCSC guidance.
• Knowledge of compute, storage, networking, security, availability, resilience, disaster recovery, and business continuity.
• Knowledge of tenant isolation, micro-segmentation, confidential computing, encryption, secure supply chains, and identity management.
• Familiarity with Terraform, Ansible, Aria Automation, or similar infrastructure automation tools.
• Strong commercial awareness and communication, presentation, analytical, organizational, and problem-solving skills.

About Rackspace Technology

Success Measures