Engineering Manager - Managed Kubernetes Services

Nutanix•San Jose, CA

2d•$201,000 - $402,000•Hybrid

About The Position

We are looking for a Senior Engineering Manager to lead the design, development, and scaling of a next-generation Kubernetes platform powering enterprise environments. This platform will serve as the foundation for AI/ML workloads, GPU infrastructure, and enterprise applications, delivering hyperscaler-like capabilities in on-prem and hybrid deployments. You will lead a team responsible for building a production-grade, globally scalable Kubernetes platform, including cluster lifecycle, fleet management, multi-tenancy, and deep integration with compute (CPU/GPU), networking, and storage systems. We are building a next-generation Kubernetes platform for enterprise AI and infrastructure, designed to bring hyperscaler-grade capabilities into on-prem environments. The team owns the full stack—from cluster lifecycle and fleet management to multi-tenancy and AI workload orchestration (including GPUs). Our charter is to enable enterprises to run mission-critical and AI workloads at massive scale, with a strong focus on simplicity, reliability, and performance. We operate as a highly collaborative, globally distributed team spanning the US and India, working closely with product, hardware, and field organizations. You will report to the Director of Engineering, who embodies a leadership style that emphasizes strong managerial expertise, technical involvement, and collaboration with engineers. The work setup for this role operates in a hybrid environment, with a strong preference for local candidates. While specific days for in-office attendance are not strictly defined, there is an expectation for the new hire to participate in office activities for certain interviews and collaborative sessions, fostering effective team interaction. As a Senior Engineering Manager, you will lead the development of a scalable, enterprise-grade Kubernetes platform that powers AI and mission-critical workloads in on-prem and hybrid environments. You will: Own end-to-end delivery of key platform capabilities, including cluster lifecycle, fleet management, and multi-tenancy Drive the design of large-scale distributed systems, evolving toward global control planes and cell-based architectures Lead a team of engineers to build AI-native infrastructure, including GPU-aware scheduling, resource isolation, and workload orchestration Partner closely with Product and cross-functional teams to translate enterprise and AI use cases into platform capabilities Establish a strong operational excellence culture, including SLOs, reliability engineering, and production readiness Simplify complex infrastructure into intuitive, consumable platform experiences for enterprise users You will play a key role in shaping a platform that brings hyperscaler-like capabilities into enterprise data centers.

Requirements

Strong Engineering Leadership
Proven experience leading and scaling high-performing engineering teams
Ability to drive clarity, ownership, and execution in complex, ambiguous problem spaces
Deep Technical Expertise
Strong understanding of distributed systems at scale
Hands-on familiarity with cloud platforms, infrastructure systems, or PaaS offerings
Experience building large, meaningful production systems (cloud platforms, infrastructure, or PaaS)
Platform & Systems Thinking
Experience designing multi-tenant platforms with clear abstractions (projects, quotas, policies)
Familiarity with multi-cluster / fleet management and large-scale system design
Ability to balance long-term architecture with near-term delivery
Execution & Operational Excellence
Track record of delivering reliable, production-grade systems
Experience with SLOs, observability, incident management, and lifecycle operations
Collaboration & Influence
Strong ability to work across product, hardware, and field teams
Effective executive-level communication and stakeholder management

Nice To Haves

Kubernetes experience is desirable, but not required—we welcome leaders who are excited to learn Kubernetes deeply and apply strong systems fundamentals to this domain
Exposure to AI/ML workloads or GPU-based systems is a plus
Equally, we welcome strong platform engineers who are excited to grow into AI infrastructure—this role offers the opportunity to learn and build in the rapidly evolving space of GPU scheduling, training, and inference systems

Responsibilities

Own end-to-end delivery of key platform capabilities, including cluster lifecycle, fleet management, and multi-tenancy
Drive the design of large-scale distributed systems, evolving toward global control planes and cell-based architectures
Lead a team of engineers to build AI-native infrastructure, including GPU-aware scheduling, resource isolation, and workload orchestration
Partner closely with Product and cross-functional teams to translate enterprise and AI use cases into platform capabilities
Establish a strong operational excellence culture, including SLOs, reliability engineering, and production readiness
Simplify complex infrastructure into intuitive, consumable platform experiences for enterprise users