Sr. Solutions Engineer

Rafay; United States; On-site; USD 140,000 - 180,000; Full time

Benefits offered by this job

Competitive salary

Robust benefits

Stock options

Job summary

Rafay seeks a Senior Solutions Engineer to partner with enterprise customers, architect and deploy cloud-native Rafay platform solutions, and optimize GPU-enabled Kubernetes environments. You will lead technical engagements, design multi-cloud deployments, and mentor junior engineers while ensuring SLAs and customer success.

Ideal candidates bring 8+ years in customer-facing roles, hands-on AI/ML workflows, and strong Kubernetes/cloud-native expertise.

Qualifications

• 8+ years in customer-facing technical roles with implementation or consulting experience.
• Hands-on AI/ML model inference and deployment workflows.
• Experience with NVIDIA GPU infrastructure (H100/H200) preferred.
• Familiar with GPU Operator, MIG, SR-IOV, and GPU networking basics.
• Knowledge of LLM inference stacks (vLLM/Triton).
• Strong Kubernetes and cloud-native expertise.
• Excellent written and verbal communication; able to lead projects.

Responsibilities

• Serve as the primary technical lead for customer implementation engagements.
• Collaborate with customers on requirements, architecture reviews, and solution design.
• Design Rafay platform capabilities across public, private, and hybrid clouds.
• Troubleshoot complex infrastructure, Kubernetes, and virtualization issues.
• Create technical docs, runbooks, and implementation guides.
• Mentor junior engineers and drive process improvements.
• Stay current on Rafay features and industry trends.

Skills

Customer-facing

AI/ML engineering

Kubernetes

Cloud-native

Linux

Networking

Troubleshooting

Documentation

Project leadership

Education

Bachelor's degree in CS/Engineering

Tools

DCGM

Prometheus

Grafana

Job description

Rafay seeks a customer-focused, technically skilled Senior Solutions Engineer.. The role involves partnering with enterprise customers/Neo clouds to architect, deploy, and optimize cloud-native infrastructure using the Rafay platform and Kubernetes ecosystems, including GPU-accelerated environments for AI/ML and inference workloads.

Key Responsibilities

• Serve as primary technical lead for customer implementation engagements
• Partner with customers on requirements gathering, architecture reviews, and solution design
• Design and configure Rafay platform capabilities across public, private, and hybrid cloud environments
• Troubleshoot complex infrastructure, networking, Kubernetes, and virtualization issues
• Collaborate with Customer Success, Product, and Engineering teams on technical challenges
• Manage customer issues within SLAs while maintaining expectations
• Reproduce and analyze customer-reported issues; communicate findings to internal teams
• Develop technical documentation, implementation guides, and runbooks
• Mentor junior engineers and contribute to process improvements
• Stay current on Rafay platform capabilities and emerging industry trends

GPU & AI/ML Infrastructure

• Deploy and configure GPU-accelerated Kubernetes clusters for AI/ML workloads, including GPU Operator, MIG, and SR-IOV setup
• Implement and validate LLM inference serving stacks (vLLM, TGI, Triton) during customer implementations
• Troubleshoot GPU cluster health, utilization, and GPU fabric/networking issues (NCCL, RoCEv2)
• Configure GPU observability using DCGM, OpenTelemetry, and related monitoring tooling
• Advise customers on distributed training and inference topology options for NVIDIA GPU infrastructure (H100/H200/B200)

Minimum Qualifications

• 8+ years of experience in customer-facing technical roles, including implementation or consulting
• Hands-on AI/ML engineering experience with model inference and deployment workflows
• Hands-on experience with NVIDIA GPU infrastructure (H100/H200/B200 preferred)
• Familiarity with GPU Operator, MIG, SR-IOV, and GPU fabric/networking fundamentals
• Experience with LLM inference serving frameworks such as vLLM, TGI, or Triton
• Strong Kubernetes and cloud-native environment expertise
• Excellent written and verbal communication abilities
• Deep troubleshooting skills across networking, virtualization, container orchestration, and public cloud platforms
• Linux systems administration and distributed systems knowledge
• Proven ability to lead technical projects and manage multiple engagements
• Bachelor's degree in Computer Science, Engineering, IT, or equivalent experience

Preferred Qualifications

• Experience with Run:AI and Slurm for GPU scheduling
• GPU autoscaling and multi-tenant GPU isolation expertise
• Familiarity with DCGM-based GPU observability and Prometheus/Grafana dashboards
• Relevant certifications (CKA, CKAD, or NVIDIA certifications

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.