Engineering Manager, Kubernetes Customer Delivery and Self-Service

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

Requirements:

• 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