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Cirrascale Cloud Services

Public IaaS for AI, multi-GPU

Manager, Data Center Development

Program ManagementUpdated on Nov 13
Full-timeAustin, TX, USAIn-person

About the Job

Requirements

  • A bachelor's degree in Engineering, Information Technology, or Business, or equivalent experience.
  • Eight to twelve years of experience in infrastructure deployment, data center operations, cloud infrastructure, managed services, or technical operations.
  • Five or more years of leadership experience coordinating technical projects or operational teams.
  • Strong understanding of data center infrastructure, including power, cooling, structured cabling, networking, server deployments, and operational workflows.
  • Experience managing complex, multidisciplinary deployment projects.
  • Strong customer-facing and vendor management experience.
  • Ability to manage multiple concurrent deployments across multiple sites.

Responsibilities

  • Own the operational deployment lifecycle from customer order through production turnover.
  • Develop deployment strategies, schedules, milestones, and implementation plans.
  • Coordinate all operational activities required to deliver customer environments.
  • Drive execution across multiple concurrent deployment projects.
  • Monitor progress, identify risks, and ensure successful project completion.
  • Conduct post-deployment reviews and capture lessons learned.
  • Develop comprehensive deployment plans for AI infrastructure, cloud platforms, and customer environments.
  • Translate customer requirements into executable deployment activities.
  • Define project scope, timelines, resource requirements, and success criteria.
  • Coordinate deployment schedules across multiple customers, sites, and business units.
  • Ensure deployment readiness before execution begins.
  • Review engineering packages for deployment readiness.
  • Validate Bills of Materials for completeness and deployment accuracy.
  • Develop and review rack elevations, floor layouts, installation drawings, and deployment documentation.
  • Coordinate structured cabling plans, rack assignments, labeling standards, and installation sequencing.
  • Verify power, cooling, floor space, and infrastructure requirements prior to deployment.
  • Coordinate factory integration, staging, burn-in, and pre-deployment validation activities.
  • Ensure operational acceptance criteria are clearly defined before production turnover.
  • Lead operational execution of customer deployments.
  • Coordinate activities between Engineering, Procurement, Logistics, Data Center Operations, Customer Success, Quality, and external partners.
  • Remove obstacles affecting deployment schedules.
  • Escalate project risks, delays, and resource constraints.
  • Ensure deployments are completed safely, efficiently, and according to company standards.
  • Coordinate deployment activities with original equipment manufacturer partners, system integrators, logistics providers, contractors, and colocation providers.
  • Manage deployment schedules with external partners.
  • Ensure vendors meet project timelines, quality standards, and operational requirements.
  • Coordinate factory acceptance testing, site acceptance testing, and turnover activities when required.
  • Ensure infrastructure is fully prepared for production operations prior to customer turnover.
  • Coordinate operational readiness reviews with Engineering, Operations, the Network Operations Center, and Customer Success.
  • Validate documentation, monitoring, asset management, operational procedures, and support readiness.
  • Ensure all operational handoffs are complete before production acceptance.
  • Develop and maintain deployment methodologies and installation standards.
  • Create and maintain Method of Procedures, Standard Operating Procedures, deployment playbooks, templates, installation guides, checklists, and operational documentation.
  • Develop rack standards, cabling standards, labeling standards, and deployment quality guidelines.
  • Standardize deployment processes across Cirrascale facilities.
  • Continuously improve deployment quality, efficiency, and scalability.
  • Identify opportunities to reduce deployment time and installation errors.
  • Standardize repeatable deployment methodologies.
  • Capture lessons learned and incorporate improvements into future deployments.
  • Develop metrics to measure deployment quality, schedule performance, and customer satisfaction.
  • Participate in customer kickoff meetings and deployment planning sessions.
  • Coordinate customer implementation schedules.
  • Provide deployment status updates to customers and executive leadership.
  • Support customer acceptance testing and production turnover.
  • Ensure successful customer onboarding into production operations.

Desired Qualifications

  • Experience supporting AI infrastructure, GPU clusters, high-performance computing, or cloud computing environments.
  • Familiarity with NVIDIA, AMD, Dell, Lenovo, Supermicro, InfiniBand, Ethernet fabrics, and high-density compute infrastructure.
  • Experience with Jira, Confluence, NetBox, Visio, Lucidchart, Microsoft Project, Smartsheet, or similar planning and collaboration tools.
  • Experience working with colocation providers and large-scale infrastructure deployments.
  • Experience developing deployment standards and operational procedures.

About the Company

Cirrascale Cloud Services

Public IaaS for AI, multi-GPU

Cirrascale Cloud Services provides dedicated AI-focused cloud infrastructure (public and private bare-metal IaaS) with flat-rate monthly pricing and no data transfer fees. Its Cirrascale AI Innovation Cloud gives access to AI accelerators from NVIDIA, Cerebras, AMD, and Qualcomm, enabling development, training, fine-tuning, and deployment on high-performance hardware with fast networking and scalable storage, plus managed services for setup and access control. It differentiates itself by offering specialized multi-GPU/bare-metal cloud with predictable pricing and no data transfer fees for large-scale operators, startups, researchers, and Fortune 500 clients. The goal is to help customers build and run AI models quickly and at scale using ready-to-use infrastructure and optional hands-on management, reducing the need for in-house DevOps and hardware management.

Company size
51-200
Company stage
Early VC
Total funding
$59.1M
Headquarters
San Diego, CA
Founded
2010

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