Cloud / DevOps Engineer, AI Compute Infrastructure

Position Summary
Richtech Robotics is looking for a Cloud / DevOps Engineer to support our AI compute infrastructure services. This role will help deploy, manage, and support cloud-based GPU environments for customers building AI models, robotics applications, simulation workflows, and Physical AI systems. The ideal candidate has strong Linux, networking, cloud infrastructure, and DevOps experience, with a willingness to learn GPU computing, CUDA environments, and AI workload deployment.

Responsibilities

• Deploy and manage cloud-based GPU compute environments for customer workloads.
• Configure virtual networks, VPNs, firewalls, security groups, SSH access, storage, and user permissions.
• Build and maintain Linux-based environments for AI development, including Docker containers, CUDA drivers, Python environments, and Jupyter notebooks.
• Work with AI engineers to deploy required runtime environments for model training, fine-tuning, simulation, and inference.
• Monitor GPU usage, system performance, uptime, storage, and network connectivity.
• Troubleshoot customer issues related to access, environment setup, networking, storage, and compute availability.
• Create reusable deployment scripts, images, templates, and technical documentation.
• Coordinate with cloud infrastructure partners and internal teams to resolve technical issues.

Required Qualifications

• 2+ years of experience in cloud infrastructure, DevOps, systems administration, or network engineering.
• Strong Linux administration skills.
• Solid understanding of networking, including TCP/IP, VPN, DNS, firewalls, routing, security groups, and private networks.
• Experience with Docker and containerized environments.
• Experience with at least one major cloud platform or private cloud environment.
• Familiarity with monitoring, logging, automation, and scripting.
• Ability to troubleshoot infrastructure issues independently.
• Strong communication skills and willingness to support customer-facing technical requests.
• Interest in learning GPU computing, CUDA environments, and AI infrastructure.

Preferred Qualifications

• Experience deploying NVIDIA GPU drivers, CUDA, cuDNN, or NVIDIA Container Toolkit.
• Familiarity with PyTorch, TensorFlow, Hugging Face, Jupyter, or vLLM.
• Experience with Slurm or distributed compute environments.
• Experience with Prometheus, Grafana, ELK, or similar monitoring tools.
• Prior experience supporting AI/ML, data science, robotics, or simulation workload

Pay: $80,000.00 - $120,000.00 per year

Benefits:

• Dental insurance
• Health insurance
• Paid time off
• Vision insurance

Work Location: In person