Nvidia Senior AI Infrastructure Engineer Cloud Partnerships - DGX 

United States, Texas 

307198966

What you’ll be doing:

• Architect unified systems for integrating infrastructure provider maintenance events into NVIDIA engineering systems

• Drive the adoption of operational excellence best practices across all infrastructure providers, partnering with SRE, infra, product, and security teams

• Define and operationalize governance models for engineering support engagements, infrastructure maintenance lifecycles, and incident escalation paths

• Measure provider availability against projected maintenance schedules using Service Level Objectives (SLOs)

• Collaborate with AI/ML teams to integrate intelligent automation into maintenance workflows, such as projecting job capacity impact based on scheduled resource availability and suggesting infrastructure reallocations for high-profile initiatives

• Develop a long-term roadmap to guide infrastructure providers in progressively adopting best practices for reliability and production hygiene across existing and new product introductions

What we need to see:

• 8+ years of experience in infrastructure architecture, cloud native, or large-scale platform/reliability roles

• Bachelor's degree or equivalent experience

• Experience designing scalable, maintainable backend systems and writing clear design documentation

• Strong understanding of multiple cloud infrastructure provider resource offerings

• Demonstrated experience in normalizing and unifying diverse data sources from a variety of systems into broadly applicable schemas, enabling efficient querying and analysis

• Proven ability to lead and influence cross-functional technical initiatives at scale across vendors and external partners, especially in reliability or platform domains

• Demonstrated ability to design and implement maintainable APIs for internal and external customers

• Proficiency in Kubernetes administration, modern CI/CD techniques and Infrastructure as Code (IaC)

• Experience building resilient production systems using Golang, Python or Ruby

Ways to stand out from the crowd:

• Proven experience in operating or architecting production systems across multiple cloud and bare-metal infrastructure providers.

• Proficiency in data science tools such as Spark, Delta Lake, and Databricks.

• Hands-on production experience with workflow engines like Temporal, Argo, or similar platforms for durable workflow execution.