Rafay
Website:
rafay.co
Job details:
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.
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