NVIDIA is looking for an outstanding AI DevOps Engineering Manager to lead and expand our next-gen inference operations infrastructure. Join us in transforming AI inference delivery, supporting NVIDIA's innovative products like Dynamo, Triton, NIXL, and our quickly growing range of AI inference solutions. This role is essential for our GitHub First initiative, enabling public CI/CD infrastructure with GPU and Kubernetes capabilities to deliver high-throughput, low-latency inferencing solutions in distributed environments. Lead a team ensuring our AI products achieve outstanding performance and reliability worldwide.
What you'll be doing:
Supervise a team of DevOps engineers with expertise in AI inference infrastructure, test automation (SDET), and Infrastructure as Code (IaC)
Architect and implement scalable test automation strategies for AI inference workloads, including performance benchmarking and automated quality gates
Lead the maintenance of our GitHub First public CI infrastructure, focusing on single/multi-GPU testing, Kubernetes multi-node GPU testing, and CSP validation
Drive Infrastructure as Code efforts by employing Terraform, Ansible, and Kubernetes to support scaling across multiple clouds and lead GPU clusters effectively.
Attain operational proficiency encompassing 24x7 on-call rotations, SRE methodologies, automated monitoring, and self-repairing systems to guarantee uptime exceeding 99.9%
Lead release coordination, cost optimization, and management of multi-cloud deployments
What we need to see:
Bachelor's/Master's degree in Computer Science, Engineering, or equivalent experience
4+ years leading DevOps/SRE organizations with direct SDET leadership experience
8+ years hands-on experience in software development, test automation, or infrastructure engineering with AI/ML or GPU-intensive workloads
Proficiency in Infrastructure as Code (IaC) platforms: Terraform, Ansible, or CloudFormation with exposure to multiple cloud environments (AWS, GCP, Azure, OCI)
Strong technical leadership in test automation frameworks, CI/CD pipeline development, and quality engineering practices
Familiarity with containerization and orchestration tools such as Docker and Kubernetes for leading AI/ML workloads and GPU resources
Proven success building and scaling teams in fast-paced, high-growth environments
Effective interpersonal skills to collaborate with remote teams and build agreement
Proficiency in Python, Rust, or related programming languages along with the capability to engage in architecture conversations
Demonstrated history of operational proficiency encompassing 24x7 on-call oversight, SRE methodologies, and robust high-availability infrastructures
Ways to stand out from the crowd:
Experience with CI/CD (specifically GitHub Actions), releasing Open-source AI software
Proficient in Deep AI/ML infrastructure with expertise in NVIDIA technologies such as CUDA, TensorRT, Dynamo and Triton Inference Server, including coordinating GPU cluster operations and GPU workload performance benchmarking
Background in DevOps, system software testing, and previous experience leading teams on inference engines, model serving platforms, or AI acceleration frameworks
Track record with monitoring tools (Prometheus, Grafana), security scanning, static/dynamic analysis tools, and license compliance automation for critical AI inferencing frameworks.
You will also be eligible for equity and benefits.
Top Skills
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
