Senior DevOps Engineer, Platform Engineering

NVIDIA•Us, CA

About The Position

NVIDIA invites applications for a Senior DevOps Platform Engineer skilled in Platform and Release Engineering to join the Metropolis team. The role involves developing, building, and maintaining foundational infrastructure and CI/CD systems that run AI/Machine Learning video analytics workloads at scale using NVIDIA Data Center GPUs. You will foster engineering rigor by setting up reliable release workflows, automation systems, and developer tools to improve efficiency on the Metropolis platform.

Requirements

BS or MS in Computer Science, Computer Engineering, or a related field, or equivalent experience, with over 6+ years of relevant industry background.
Advanced skills in Python for scripting, tooling, and automation.
Deep expertise with Kubernetes, Helm, and container orchestration in production environments.
Verified background in building and maintaining CI/CD pipelines at scale (Jenkins, GitHub/GitLab Actions and Runners, or similar).
Solid understanding of Linux systems administration, networking, and distributed systems.
Experience with release engineering practices including semantic versioning, release gating, and change management.
Hands-on experience with observability stacks (Prometheus, Grafana, ELK, or similar).

Nice To Haves

Experience with GPU infrastructure and AI/ML platform engineering at scale.
Background in BareMetal and hybrid cloud (AWS, GCP, Azure) environment management.
Familiarity with NVIDIA Metropolis, DeepStream, or similar AI video analytics platforms.
Experience with GitOps workflows, Infrastructure as Code (Terraform, Ansible).
Track record of driving DevOps culture transformation and developer experience improvements.

Responsibilities

Compose, build, and maintain scalable CI/CD pipelines using Jenkins, GitHub/GitLab Actions and Runners for Metropolis software products.
Develop and manage Kubernetes-based platform infrastructure supporting AI/ML workloads on NVIDIA Data Center GPUs.
Build and implement scaling and performance measurement frameworks within Kubernetes to ensure platform reliability and efficiency under AI/ML workload demands.
Define and implement release engineering processes, branching strategies, versioning standards, and gating criteria.
Drive developer efficiency by building and maintaining DevOps MCP servers, tooling, and automation frameworks.
Own observability and monitoring infrastructure using Prometheus, Grafana, and log aggregation pipelines.
Troubleshoot hardware and operating system issues across BareMetal and GPU-accelerated servers to minimize downtime and maintain platform stability.