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IONOS SE is seeking a senior/staff-level engineer to operate large production networks across multiple data centers with reliability at the core. You will define health metrics, lead major incidents, and collaborate with NRE Automation to automate operations.
Fluent English is required; German is a plus. You will apply AI to network tasks, build automation using Python, Ansible, and CI/CD on GitLab, and help strengthen observability and runbooks across global teams.
At IONOS, we don't just manage servers – we shape the digital future for more than 6.2 million customers worldwide with our solutions. As Europe's leading hosting provider and a pioneer in independent cloud solutions, we are building next-generation infrastructure – from sovereign cloud architectures and high-performance GPU clusters to integrated AI automation tools.
What drives us is digital sovereignty for Europe and real impact for small and medium-sized businesses (SMBs) as well as large enterprises. We work in agile teams, rely on transparent structures, and believe that excellence and innovation only emerge through true team spirit.
Ready for your next step? Become part of IONOS and grow with us.
NRE Operations runs the data center networks for the entire IONOS group: the physical fabric and underlay, the SDN overlay, and the virtual network functions on top, across dozens of data centers. We are looking for a senior or staff-level engineer who operates large production networks with a reliability mindset. You define what "healthy" means, catch problems before customers do, lead the hardest incidents, and partner with NRE Automation to push routine operations toward zero manual steps. AI is central to how we work, both to make the network smarter and to make each engineer faster.
We expect every engineer here to be fluent with AI, and we mean it in two concrete ways. First, applying AI to the network itself: anomaly detection, automated diagnosis and root-cause analysis, config generation and validation, smarter alerting, and runbook or agent-driven automation. Second, using AI to raise your own output: coding assistants and LLM tooling for scripting, troubleshooting, research, and documentation. Reliability work punishes blind trust, so the skill that matters most is judgment. You need to know when an AI-generated config or diagnosis is trustworthy and when it is not.