Senior Solutions Engineer

Rafay Systems; United States; On-site; USD 140,000 - 170,000; Full time

Job summary

Rafay Systems seeks a Senior Solutions Engineer who partners with enterprise customers to architect, deploy, and optimize Rafay platform-based cloud-native infrastructure and Kubernetes ecosystems.

The role focuses on leading customer engagements, designing scalable solutions, and guiding teams through GPU-accelerated AI/ML workloads with performance and reliability in mind. Strong communication is essential.

Qualifications

• 10+ 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 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.

Responsibilities

• Lead customer implementation engagements.
• Gather requirements, perform architecture reviews, and design solutions.
• Design Rafay platform deployments across public, private, and hybrid clouds.
• Troubleshoot complex infra, networking, Kubernetes, and virtualization issues.
• Collaborate with CS, Product, and Engineering on technical challenges.
• Manage customer issues within SLAs and set expectations.
• Create technical docs, runbooks, and implementation guides.
• Mentor junior engineers and drive process improvements.
• Stay current on Rafay capabilities and industry trends.
• Deploy GPU-accelerated Kubernetes clusters for AI/ML workloads with GPU Operator, MIG, and SR-IOV setup.
• Validate LLM inference stacks (vLLM, TGI, Triton).
• Troubleshoot GPU cluster health, utilization, and GPU fabric networking.
• Configure GPU observability with DCGM and OpenTelemetry; use Grafana dashboards.
• Advise on distributed training and inference topology for NVIDIA GPUs.

Skills

Customer-facing experience

Technical leadership

Kubernetes

Cloud-native architecture

AI/ML engineering

Linux administration

NVIDIA GPU infrastructure

LLM inference serving

Education

Bachelor's degree in Computer Science, Engineering, IT, or equivalent experience

Tools

NVIDIA GPU Operator

MIG

SR-IOV

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

Core Implementation 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

• 10+ 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.