AI/HPC Systems Engineer — GPU Cluster & Hybrid Cloud

Saige Partners

San Jose (CA)

On-site

USD 140,000 - 190,000

Full time

13 days ago

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Job summary

Saige Partners seeks an AI/HPC Systems Engineer to build, deploy, and operate GPU-enabled compute infrastructure across on-premises and cloud platforms. You will automate provisioning, monitor performance, and support AI/ML workloads for engineering and R&D teams.

The role focuses on GPU/HPC clusters, hybrid cloud, and observability, collaborating with IT and engineering to deliver reliable systems at scale.

Qualifications

Bachelor's degree in Computer Science, Engineering, or a related technical field.
3 years of hands-on IT infrastructure, cloud, HPC, or related experience.
Experience with Linux-based infrastructure and public clouds (AWS, Azure, or GCP).
Experience deploying, configuring, or operating GPU and/or HPC environments.
Experience with workload scheduling or orchestration such as Kubernetes, Slurm, or similar.
Experience with infrastructure automation, monitoring, troubleshooting, and performance optimization.
Strong understanding of compute, storage, networking, virtualization, and container technologies.
Experience supporting AI/ML infrastructure or workloads is a plus.
Strong problem-solving, collaboration, and communication skills.
Ability to work effectively across engineering, R&D, and IT teams.

Responsibilities

Build, configure, and operate GPU and HPC clusters across compute, storage, and networking environments.
Support capacity planning, performance tuning, and infrastructure optimization for AI training, inference, and compute-intensive workloads.
Monitor system performance, availability, and resource utilization to ensure reliable operations.
Deploy and maintain computing environments across on-premises infrastructure and public cloud platforms.
Support infrastructure modernization, expansion, and scaling initiatives for HPC and AI workloads.
Help evaluate and implement solutions that improve scalability, reliability, and cost efficiency.
Implement infrastructure-as-code and automated provisioning solutions.
Develop and maintain monitoring, logging, alerting, and observability capabilities.
Automate routine infrastructure tasks and identify opportunities to improve resource utilization and operational efficiency.
Deploy, integrate, and support LLM APIs, coding assistants, and AI/agent platforms used by engineering teams.
Assist with the infrastructure requirements and operational support of AI/ML workloads.
Collaborate with engineering teams to ensure AI platforms are reliable, accessible, and scalable.
Troubleshoot and resolve infrastructure, networking, compute, storage, and platform issues.
Develop and maintain technical documentation, standards, procedures, and operational runbooks.
Collaborate with engineering, IT, and other stakeholders to deliver reliable infrastructure solutions.