AI Infrastructure Engineer - NVIDIA GPU
About The Position
In this role, you’ll make an impact in the following ways: Be hands-on with enterprise-grade NVIDIA AI infrastructure, supporting GPU-based compute, high-performance storage, and network systems designed for ML/AI at scale. Deploy, monitor, and troubleshoot containerized AI workloads using Kubernetes, Docker, and GPU orchestration tools like Run:AI and NVIDIA BCM. Own the observability of our AI platforms—monitor health, identify performance bottlenecks, and make strategic recommendations to drive platform reliability and maturity. Automate infrastructure operations and provisioning using Python, Bash, and tools like Terraform or Ansible to reduce manual toil and accelerate experimentation. Maintain and scale AI training and inference pipelines, integrating infrastructure workflows into CI/CD systems to enable seamless, automated deployment of AI workloads.
Requirements
- Bachelor's degree in computer science or a related discipline, or equivalent work experience required; advanced degree preferred8-10 years of related experience required; experience in the securities or financial services industry is a plus.
- Experience with Linux administration (RHEL/Ubuntu), shell scripting, and system-level debugging.
- Proven experience running distributed systems in Kubernetes and containerized environments using Docker.
- Familiarity with GPU resource management, including NVIDIA GPU Operator and device plugin lifecycle.
- Experience with CI/CD workflows and infrastructure automation tools such as GitLab CI, Jenkins, Terraform, Helm, or Ansible.
- Knowledge of networking fundamentals and persistent storage systems.
- Exposure to cloud platforms (AWS, GCP, Azure) and hybrid GPU environments.
- Ability to read and support Python code focused on ML/AI pipeline integration.
- Strong analytical and troubleshooting skills with a collaborative mindset.
- Effective communication skills and proactive ownership of platform reliability and performance.
Responsibilities
- Be hands-on with enterprise-grade NVIDIA AI infrastructure, supporting GPU-based compute, high-performance storage, and network systems designed for ML/AI at scale.
- Deploy, monitor, and troubleshoot containerized AI workloads using Kubernetes, Docker, and GPU orchestration tools like Run:AI and NVIDIA BCM.
- Own the observability of our AI platforms—monitor health, identify performance bottlenecks, and make strategic recommendations to drive platform reliability and maturity.
- Automate infrastructure operations and provisioning using Python, Bash, and tools like Terraform or Ansible to reduce manual toil and accelerate experimentation.
- Maintain and scale AI training and inference pipelines, integrating infrastructure workflows into CI/CD systems to enable seamless, automated deployment of AI workloads.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Number of Employees
251-500 employees
