Manager, AI Cluster Management & Automation

Role summary

Manager, AI Cluster Management & Automation is a full-time role at Lenovo in Morrisville, North Carolina, United States (on-site). Posted 2026-11-03. Experience level: 10+.

Job title

Manager, AI Cluster Management & Automation

Company

Lenovo

Location

Morrisville, North Carolina, United States

Work arrangement

on-site

Employment type

full-time

Category

Software Engineering

Experience level

10+

Date posted

2026-11-03

Job description

Why Work at Lenovo

We are Lenovo. We do what we say. We own what we do. We WOW our customers.

Lenovo is a US$83 billion revenue global technology powerhouse, ranked #153 in the Fortune Global 500, and serving millions of customers every day in 180 markets. Focused on a bold vision to deliver Smarter Technology for All, Lenovo has built on its success as the world’s largest PC company with a full-stack portfolio of AI-enabled, AI-ready, and AI-optimized devices (PCs, workstations, smartphones, tablets), infrastructure (server, storage, edge, high performance computing and software defined infrastructure), software, solutions, and services. Lenovo’s continued investment in world-changing innovation is building a more equitable, trustworthy, and smarter future for everyone, everywhere. Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992) (ADR: LNVGY).

This transformation together with Lenovo’s world-changing innovation is building a more inclusive, trustworthy, and smarter future for everyone, everywhere. To find out more visit www.lenovo.com , and read about the latest news via our StoryHub .

Description and Requirements

Job Title : Manager, AI Cluster Management & Automation

Job Type : Development Engineering Manager

Band : 8

Employment Type : Full-time

Location : Morrisville, NC

Position Overview

We are seeking an experienced engineering manager to lead the technical strategy, architecture, integration, and development of AI cluster management, deployment, validation, and automation software, leveraging open-source AI infrastructure as the software foundation.

This 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 successful candidate will lead development of scalable software and workflows that enable 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. This role combines software architecture, AI/HPC infrastructure expertise, project delivery, and people leadership to build scalable, reliable cluster-deployment and validation solutions.

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.

Important: This is a bare-metal AI infrastructure engineering leadership role. Successful candidates will have experience deploying, validating, automating, and operating physical GPU clusters, servers, networking, and storage infrastructure. Experience limited to provisioning cloud-based virtual infrastructure (AWS, Azure, GCP, or similar public-cloud environments) without significant bare-metal deployment and validation experience will generally not meet the requirements of this position.

Key Responsibilities:

1. Technical and Engineering Leadership

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 supporting production-scale AI and HPC environments.

Define modular, API-driven architectures spanning discovery, provisioning, configuration, observability, validation, and lifecycle management; make explicit 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.

2. Platform Integration

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 to the deployment architecture.

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 to support 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.

Integrate Ansible-based infrastructure automation.

Integrate Kubernetes-based orchestration and selected GPU/network operators, defining clear 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