Service Delivery Manager
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About Nebius:
Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.
Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.
Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.
The role
We are looking for a Service Delivery Manager to support strategic hyperscale customers using bare metal and GPU cloud AI infrastructure in Nebius data centers.
- Amsterdam, Netherlands
This role acts as the technical and operational interface between the customer/platform and Nebius infrastructure teams, ensuring reliable service delivery, SLA compliance, and smooth operations of large-scale GPU clusters and bare metal environments.
You will coordinate across data center operations, network, hardware lifecycle, and infrastructure engineering teams to deliver world-class infrastructure services for large AI workloads.
Your responsibilities will include:
Customer Infrastructure Ownership
- Serve as the primary technical point of contact for the customer.
- Manage operational relationship with hyperscale customers.
- Coordinate infrastructure lifecycle including provisioning, maintenance, and incident management.
Service Delivery & SLA Management
- Ensure SLA and SLO compliance for infrastructure services.
- Drive incident management and root cause analysis.
- Track service performance and operational metrics.
Data Center & Infrastructure Coordination
- Work closely with data center operations teams to manage hardware support and infrastructure maintenance.
- Coordinate bare metal deployments, replacements, and capacity expansion.
- Align infrastructure operations with customer workload requirements.
Operational Excellence
- Establish operational processes for hyperscale infrastructure environments.
- Lead service reviews and operational planning with internal and customer teams.
- Improve reliability, response times, and operational workflows.
Cross-Functional Collaboration
- Partner with engineering, networking, and hardware lifecycle teams.
- Support infrastructure scaling and new cluster deployments.
- Participate in planning for large-scale AI compute infrastructure.
We expect you to have:
- 5+ years in technical account management, service delivery, or infrastructure operations
- Experience working with hyperscale customers or large enterprise clients
- Background in cloud, AI infrastructure, or data center operations
- Understanding of bare metal infrastructure
- Experience with data center environments
- Familiarity with GPU clusters / AI workloads (preferred)
- Knowledge of networking, hardware lifecycle, and infrastructure monitoring
- Strong stakeholder management
- Ability to operate in high-scale infrastructure environments
- Excellent communication between technical and business teams
It will be an added bonus of you have:
- Experience working with hyperscale companies
- Experience supporting AI / ML infrastructure
- Experience with GPU clusters
Benefits & Perks:
- Competitive compensation
- Career growth and learning opportunities
- Flexibility and ownership
- Collaborative and innovative culture
- Opportunity to work on impactful AI projects
- International environment and talented teams
Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire.
Nebius provides AI cloud infrastructure, offering a full-stack cloud platform for AI developers, enterprises, and research institutes to build and deploy generative AI applications and train and run machine learning models in a secure, high-performance, and cost-optimized environment.