Note: The job is a remote job and is open to candidates in USA. NVIDIA’s DGX Cloud Kubernetes Platform & Production Engineering team is seeking an Engineering Manager to lead its Customer Delivery and Self-Service function. The role is responsible for overseeing end-to-end delivery of production Kubernetes clusters for AI workloads, building cross-functional integrations, and transforming delivery processes into scalable, automated self-service workflows. The manager will also set the team’s roadmap, hire and develop engineers, and improve delivery speed, operational readiness, and customer visibility.
Responsibilities
- Build and lead a team of software and production engineers passionate about Kubernetes customer delivery, onboarding, and self-service
- Own end-to-end delivery of production Kubernetes clusters for AI workloads, from accepted request through enablement, qualification, validation, and customer handoff
- Drive a coordinated delivery plan with clear owners, dependencies, readiness gates, timelines, risks, status, and blocking issues
- Partner across platform, runtime, release, fleet operations, CSE, product, TPM, security, and infrastructure teams
- Build integrations connecting customer intake and status systems with Kubernetes provisioning, access, validation, and production acceptance
- Turn recurring delivery tasks into detailed, automated self-service workflows using APIs, AI tools, and agents
- Define service interfaces and measure and improve delivery speed, readiness, automation, recovery, and customer visibility
- Set the team’s roadmap, staffing, and operational ownership while hiring, mentoring, and developing technical leaders
Skills
- 8+ overall years of industry experience, including 2+ years leading or managing engineers
- Experience building platform APIs, self-service infrastructure, workflow automation, developer platforms, or customer onboarding systems
- Strong understanding of Kubernetes, cloud infrastructure, distributed systems, or production engineering
- Hands-on experience using AI coding tools and AI-enabled engineering workflows
- Experience integrating multiple systems and teams into a reliable end-to-end workflow
- Ability to translate customer and operational requirements into clear technical interfaces and automated solutions
- Strong cross-functional leadership, communication, customer empathy, prioritization, and judgment
- BS or MS in Computer Science, Engineering, or equivalent experience
- Experience building Kubernetes provisioning, infrastructure-as-code, service catalog, or internal developer platform capabilities
- Familiarity with Terraform, GitOps, identity and access management, RBAC, APIs, workflow engines, and production-readiness automation
- Experience with GPU infrastructure and AI-optimized Kubernetes clusters, including accelerated networking, high-performance storage, GPU scheduling, workload qualification, or large-scale fleet operations
- A track record of reducing onboarding time and operational toil through automation and self-service
- Experience combining strong platform engineering with an attitude centered on product development and customer needs
Benefits
- You will also be eligible for equity.
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