DevOps / Platform Engineer — Scientific Computing

Posted 4 Days Ago
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Hiring Remotely in Germany
Remote
Entry level
Information Technology
The Role
Owns build, deployment, infrastructure, and release processes for scientific software across cloud, on-premise, restricted, and air-gapped environments. Responsibilities include Kubernetes and GitOps operations, CI/CD, offline installers and updates, database migrations, secrets and access management, infrastructure-as-code, monitoring, security scanning, backups, recovery, and compliance support. The role requires production-scale AWS or equivalent experience, Kubernetes, Ansible, offline deployment expertise, and end-to-end pipeline ownership.
Summary Generated by Built In

Location: Remote within Europe, with regular presence in Bremen

Direct Reports: None

Working Model: Remote / hybrid. Regular time on-site in Bremen.

Mission

Own build, deployment and infrastructure for scientific software that runs in awkward places — customer premises, restricted laboratory networks, air-gapped sites, and our own cloud. The role combines conventional cloud platform engineering with the harder problem of shipping updatable software into environments we do not control.

Responsibilities

Pillar 1 Deployment and Delivery

  • Own build, CI/CD and release for the platform across cloud and on-premise targets.
  • Build and maintain installers and update paths that work in restricted and offline environments.
  • Manage database migration and rollback paths for customer installations.

Success Metrics and KPIs: release frequency, failed deployments, time to patch a customer site.

Pillar 2 Infrastructure

  • Run the Kubernetes and GitOps side of the platform.
  • Own secrets management, access control and infrastructure-as-code.
  • Implement monitoring and log aggregation across internal and customer environments.
  • Integrate with Constructor's platform, identity and deployment tooling.

Success Metrics and KPIs: uptime, mean time to detect, infrastructure reproducible from code.

Pillar 3 Security and Governance

  • Introduce static analysis and dependency and licence scanning into the pipeline.
  • Own backup, recovery and continuity for the platform.
  • Support audit and compliance requirements from funded research and customer contracts.

Accountability: no single undocumented dependency on one person, including yourself.

Requirements

Experience

  • Strong AWS or equivalent, Kubernetes, and configuration management such as Ansible, at production scale.
  • Real experience of on-premise and offline deployment, not only cloud.
  • CI/CD pipeline ownership end to end

Skills & Competencies

  • Autonomous and proactive. You have carried infrastructure alone.
  • Willing to hand knowledge over rather than hold it.
  • Interested in the product and the science, not only the pipeline.
  • Clear communicator in English.

Education & Languages

  • Degree in computer science or engineering, or equivalent practical track record.
  • Fluent English.
What This Role Is (and Is Not)

This role IS

  • Full ownership of infrastructure for a technically unusual product.
  • A role where you finally get a team around you.
  • Exposure to a broad modern stack.

This role is NOT

  • A pure cloud or pure on-premise role.
  • A ticket-driven operations role.
  • A role without contact with the product.

Skills Required

  • Strong production-scale experience with AWS or an equivalent cloud platform
  • Production-scale Kubernetes experience
  • Production-scale configuration management experience, such as Ansible
  • Experience deploying software on-premise and in offline or air-gapped environments
  • End-to-end ownership of CI/CD pipelines
  • Autonomous and proactive infrastructure ownership
  • Clear communication in English
  • Degree in computer science or engineering, or equivalent practical experience
  • Fluent English
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The Company
Year Founded: 2024

What We Do

Constructor Knowledge Labs (CKL) is a research institute based in Bremen, Germany, dedicated to advancing applied research in Computer Science, Software Engineering, Machine Learning, and Artificial Intelligence across interdisciplinary domains.

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