Manager, AI Cluster Management & Automation

Lenovo•Morrisville, NC

5d•Onsite

About The Position

Lenovo is seeking an experienced engineering manager to lead the technical strategy, architecture, integration, and development of AI cluster management, deployment, validation, and automation software, using open-source AI infrastructure as its foundation. The role focuses on bare-metal AI/HPC infrastructure deployment and lifecycle management, including physical server, GPU, networking, storage, and data center infrastructure bring-up, provisioning, validation, automation, and operational readiness. The manager will lead development of scalable software and workflows for automated deployment, validation, monitoring, and lifecycle management of production AI clusters. ACTOR is the cluster automation and validation software layer that integrates infrastructure components into end-to-end deployment, testing, certification, and lifecycle-management workflows. The position combines software architecture, AI/HPC infrastructure expertise, project delivery, and people leadership. The manager will collaborate with solution architects, GPU/server engineering, networking, storage, data center infrastructure, services, customers, and technology partners including NVIDIA and AMD, while maintaining clear integration boundaries for AMD and other supported environments. This is a bare-metal AI infrastructure engineering leadership role; cloud-only virtual infrastructure provisioning experience without significant bare-metal deployment and validation experience will generally not meet the position's requirements.

Requirements

Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field.
At least 8 years of experience in software engineering, infrastructure engineering, cluster management, distributed systems, AI/HPC infrastructure, or related areas.
Experience leading technical projects and/or engineering teams.
Strong software architecture and system-design skills, including modular integration, API contracts, reuse-versus-build decisions, and component lifecycle management.
Hands-on experience deploying, managing, and troubleshooting Linux-based bare-metal infrastructure in AI, HPC, data center, or large-scale distributed computing environments.
Programming or scripting experience with one or more languages such as Python, Go, Rust, C/C++, Bash, or comparable languages.
Experience integrating infrastructure platforms through APIs and automation/configuration technologies such as Ansible, including automated testing, release management, and compatibility validation.
Strong knowledge in one or more of these areas: AI/HPC cluster management, bare-metal infrastructure automation, GPU cluster operations, infrastructure or network automation, DCIM, server provisioning and lifecycle management, or hardware validation and cluster qualification.
Understanding of modern AI/HPC infrastructure, including GPU compute, high-performance networking, storage, cluster management, and physical infrastructure deployment.
Experience designing, deploying, validating, or automating bare-metal AI, HPC, or data center infrastructure, including physical servers, GPU platforms, networking, storage, and cluster-management systems.
Experience with hardware provisioning, platform bring-up, firmware validation, infrastructure acceptance testing, deployment automation, or production infrastructure qualification processes.
Strong communication, technical leadership, and cross-functional collaboration skills.

Nice To Haves

Experience with NVIDIA GPU infrastructure, HGX, DGX, NVL-class systems, AI factories, or AMD GPU clusters.
Familiarity with open-source AI infrastructure and modular software ecosystems, or hands-on experience with NVIDIA BCM, NICo, Mission Control, Kubernetes GPU/Network Operators, DCGM, or comparable cluster-management technologies.
Strong hands-on experience with Redfish, BMC/OpenBMC, PXE-based provisioning, MAAS, xCAT, Foreman, Ironic, Canonical MAAS, or similar bare-metal deployment and lifecycle-management technologies.
Experience with NetBox, DCIM, digital-twin platforms, infrastructure inventory systems, asset-management platforms, or topology-management solutions.
Understanding of InfiniBand, RoCE, Ethernet, and large-scale GPU-cluster networking.
Experience integrating or extending production-scale infrastructure-management, Kubernetes, Slurm, or GPU orchestration platforms, including multi-tenant isolation, observability, security, lifecycle management, and operational resilience.
Experience with MLPerf, NCCL testing, GPU diagnostics, burn-in testing, cluster acceptance testing, hardware qualification, platform certification, or deployment-validation frameworks.
Experience validating, deploying, or operating NVIDIA HGX, DGX, NVL platforms, AMD GPU clusters, HPC clusters, or large-scale accelerated-computing environments.
Experience working with hyperscalers, neocloud providers, cloud service providers, enterprise customers, or large-scale data center operators.
Experience supporting rack-scale AI infrastructure deployment, cluster bring-up, server qualification, firmware validation, or production deployment certification activities.

Responsibilities

Own the technical roadmap and software architecture for a cluster-validation solution built on open-source AI infrastructure, defining responsibilities across infrastructure components, ACTOR, and partner systems.
Lead ACTOR development as an integration and workflow layer, prioritizing hardware enablement, deployment automation, validation, and customer-specific capabilities that complement the underlying open-source AI infrastructure.
Lead software architecture and automation for bare-metal cluster deployment, hardware validation, infrastructure qualification, cluster bring-up, and acceptance testing for production-scale AI and HPC environments.
Define modular, API-driven architectures spanning discovery, provisioning, configuration, observability, validation, and lifecycle management; make reuse, integration, extension, and build decisions to avoid duplicating platform capabilities.
Establish engineering standards, design reviews, automated integration testing, and release practices, including component compatibility, upgrade and rollback strategies, security, and upstream change management.
Drive technical decisions around deployment repeatability, validation coverage, observability, operational resilience, and lifecycle-management automation for large-scale AI cluster environments.
Lead adoption and integration of selected open-source AI infrastructure components for infrastructure operations, accelerated Kubernetes, observability, security, health, and resilience; integrate NVIDIA Network Config and NICo where applicable.
Maintain modular adapters for AMD cluster-management environments and other supported platforms, validating cross-vendor compatibility of selected open-source AI infrastructure components.
Integrate Redfish, BMC, OpenBMC, and related management interfaces for bare-metal server provisioning, hardware inventory discovery, firmware management, power control, diagnostics, cluster bring-up, and lifecycle operations.
Integrate NetBox and data center infrastructure management (DCIM) systems, as well as Ansible-based infrastructure automation.
Integrate Kubernetes-based orchestration and selected GPU/network operators, defining ownership between infrastructure automation and workload platforms.
Integrate Slurm and applicable GPU orchestration platforms, and connect Prometheus/Grafana with platform telemetry rather than creating parallel scheduling or monitoring systems.
Develop versioned integrations using REST APIs and infrastructure-management interfaces, with authentication, authorization, error handling, and compatibility testing.
Design and automate end-to-end cluster deployment workflows spanning rack integration, hardware discovery, network configuration, operating system provisioning, GPU enablement, acceptance testing, validation, and operational handoff.
Develop validation frameworks for physical AI infrastructure, including server qualification, GPU validation, networking verification, storage integration testing, cluster health assessment, deployment certification, and production-readiness testing.
Connect cluster health monitoring, diagnostics, and telemetry to actionable validation and remediation workflows, with safe execution controls and clear operational handoffs.
Manage engineers delivering platform integration, cluster automation, networking, GPU operations, OS/platform software, and infrastructure management.
Translate the roadmap into engineering priorities, integration milestones, and clear assignments, focusing custom development on differentiated capabilities.
Lead project execution against program milestones and software release objectives, tracking deployment repeatability, integration quality, hardware-validation readiness, and validation coverage.
Coordinate dependencies across internal engineering, solution teams, NVIDIA, AMD, and other partners, including platform changes, defect escalation, and support boundaries.
Balance near-term customer commitments with a maintainable platform roadmap, reducing fragmented integrations and long-term upgrade burden.
Lead engineering efforts supporting deployment readiness and validation of production AI clusters, ensuring repeatable, scalable, and automated installation, qualification, and certification across customer and data center environments.
Develop and mentor engineering talent while fostering technical excellence, collaboration, accountability, and continuous improvement.