Manager, Solution Architect

Manager, Solution Architect

About the Role

Rafay seeks a Principal Solutions Architect to enable enterprise customers/Neo clouds in deploying, operating, and scaling AI/ML workloads on our GPU Platform-as-a-Service offering. This is a hybrid technical leadership and people-management role: alongside hands-on, customer-facing architecture work, this person will build, lead, and grow a team of Solutions Architects, collaborating with platform engineering, MLOps, data science, and infrastructure teams to architect production-ready AI infrastructure solutions built on Kubernetes and GPU-accelerated environments.

Key Responsibilities

Team Leadership & People Management

• Recruit, hire, and onboard Solutions Architects as the team scales
• Directly manage a team of Solutions Architects, including workload allocation, coaching, and day-to-day support
• Set individual and team goals; conduct regular 1:1s and performance reviews
• Own career development planning for direct reports, including skills growth, promotion readiness, and succession planning
• Foster an inclusive, high-performing team culture aligned with Rafay's values
• Manage team capacity, prioritization, and staffing against customer and project demand
• Partner with sales, engineering, and executive leadership on hiring plans and team structure
• Mentor and upskill both direct reports and junior team members across the broader organization

Technical & Customer-Facing Responsibilities

• Design comprehensive AI/ML platform architectures covering inference, training, and data pipelines
• Develop reference architectures for GPU cluster deployment and LLM serving infrastructure
• Evaluate inference serving frameworks including vLLM, TGI, and Triton
• Advise on GPU fabric topology options for distributed training scenarios
• Design observability strategies using DCGM, OpenTelemetry, and eBPF
• Translate infrastructure requirements into actionable platform designs
• Deliver technical presentations, workshops, and proof-of-concept engagements
• Serve as trusted advisor on AI infrastructure strategy, cost optimization, and scaling
• Partner with customer stakeholders to understand workload requirements
• Architect networking, identity management, observability, and security integrations
• Monitor and troubleshoot production environments for GPU utilization and cluster health
• Lead root cause analysis for complex customer issues
• Document reference architectures and implementation best practices

Required Qualifications

• 8+ years in infrastructure, platform, or solutions engineering roles
• 3+ years focused on AI/ML infrastructure or MLOps
• 2+ years of direct people-management experience, including hiring, performance management, and career development of technical staff
• Demonstrated ability to lead and grow a technical team while remaining hands-on with customers and architecture
• Deep Kubernetes expertise including cluster lifecycle and RBAC
• Hands-on experience with NVIDIA GPU infrastructure (H100/H200/B200 preferred)
• Proficiency with distributed training concepts (NCCL, tensor parallelism)
• Experience with LLM inference serving and optimization
• Familiarity with GPU Operator, MIG, SR-IOV, and network fabrics
• Strong scripting and automation skills (Python, Bash, Go preferred)
• Ability to communicate complex technical concepts to diverse audiences, including executive stakeholders
• Experience with AWS, Azure, or GCP platforms
• Familiarity with monitoring tools like Prometheus, Grafana, and OpenTelemetry
• Understanding of GPU-based workloads and model serving
• Proven troubleshooting capabilities for infrastructure issues
• Excellent communication, coaching, and customer-facing skills

Preferred Qualifications

• Experience building a Solutions Architecture or technical pre-sales team from the ground up
• Formal people-management training or leadership certification
• Enterprise customer support experience in cloud-native environments
• Familiarity with PyTorch and TensorFlow frameworks
• Experience with Run:AI and Slurm
• GPU scheduling and autoscaling expertise
• Multi-tenant Kubernetes environment knowledge
• MLOps platform experience
• Technical workshop leadership experience
• Relevant certifications (CKA, CKAD, AWS/Azure/GCP Solutions Architect)
• Understanding of multi-tenant GPU isolation technologies

Why Join Rafay?

Rafay is at the forefront of GPU PaaS technologies and Kubernetes and we offer unique opportunities to join a winning team working on foundational technology for cloud and AI/ML services and enterprises. We work in a collaborative environment that rewards creative thinking and provides opportunities to advance professional careers in advanced technology development. On top of this we offer a fun and dynamic work environment, a competitive salary, robust benefits and attractive stock options. As the first of our kind, we are truly in a class of our own.