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# Cloud & Infrastructure Engineer

- **Company:** CinSOIL GmbH
- **Location:** Bremen, Germany (Remote available)
- **Salary:** €65,000.0 - €85,000.0
- **Contract:** Permanent contract
- **Skills:** Geographic Information Systems, Data Analysis, Cloud Computing, Linux, Machine Learning, NetCDF, Containerization, Git Flow, Kubernetes, Machine Learning Operations, Docker
- **Published:** August 10, 2026
- **Apply:** https://de.indeed.com/viewjob?jk=dbb0151c5d4d5394

## About the Role

Required:

 * Solid experience with cloud infrastructure and infrastructure-as-code

 * Experience with Docker and container orchestration (Kubernetes or similar)

 * Comfortable with cost modelling and making pragmatic build-vs-buy tradeoffs

 * Strong Linux/bash skills and Git-based workflows

 * Ability to work independently and make sound architectural calls with limited oversight

Nice to have:

 * Experience with GPU/HPC infrastructure for ML model training

 * Familiarity with geospatial data formats and workloads (large raster/NetCDF datasets, cloud-optimised formats)

 * Experience setting up infrastructure for a small startup or research team (not just maintaining an existing enterprise setup)

 * Exposure to openEO, CDSE, or similar EO-specific cloud platforms

We're open to a range of seniority here, what matters most is sound judgement and the ability to make good infrastructure decisions independently, not a specific number of years.

## Description

You will own the infrastructure that our EO analytics, modelling, and AI pipelines run on. This is a hands-on role with real architectural ownership at a small technical team where you'll be building the foundation, not maintaining someone else's.

This position is contingent on the finalisation of an upcoming ESA contract, whose deliverables involve close collaboration with the contract's stakeholders.

What You'll Work On

 * Evaluating and implementing our compute strategy: cloud vs. on-prem/dedicated servers for compute-intensive model training and EO processing

 * Designing and maintaining CI/CD pipelines, containerisation (Docker, Kubernetes), and infrastructure-as-code

 * Setting up scalable storage for large geospatial datasets (COG, Zarr, STAC-based catalogues)

 * Cost modelling and optimisation across our tech stack

 * Supporting a small, mixed team of freelancers, interns, and researchers with reliable dev/compute environments


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