Senior Software Engineer, Kubernetes Automation
New
C
Cast AICloud infrastructure
We are currently accepting applications from candidates residing in the following European countries: Bulgaria, Croatia, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Cyprus, Malta, Finland, Sweden, Ireland, Moldova, North Macedonia, Montenegro, Slovenia, and Ukraine.Full-TimeSenior
Salary€6,500 - €9,000 gross, depending on the level of experience
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Job Details
- Languages
- Strong English skills, both verbal and written.
- Required Skills
- AWSPostgreSQLGCPKubernetesAzureClickhouseGogRPCPrometheusTerraform
Requirements
- Strong software engineering fundamentals, including designing clean abstractions, writing maintainable code, and justifying architecture choices.
- Production experience with Go is strongly preferred; candidates without Go should demonstrate strong systems programming skills in a comparable language.
- Understanding of Kubernetes internals, including autoscaling and networking.
- Experience personally driving a complex project end-to-end.
- Experience with AI coding agents and agentic development workflows.
- Experience using observability tooling in production.
- Experience with CI/CD and DevOps practices.
- Strong verbal and written English skills.
- Hands-on experience with cloud platforms such as AWS, GCP, or Azure is a nice-to-have.
- Deep knowledge of EKS, GKE, or AKS internals is a nice-to-have.
- Experience with container runtimes such as CRI-O, runc, or containerd is a nice-to-have.
- Knowledge of cloud storage, including block storage, CSI, volume management, or filesystem internals, is a nice-to-have.
Responsibilities
- Design and build distributed systems that operate Kubernetes infrastructure autonomously at scale.
- Write production Go services that interact with AWS, GCP, and Azure APIs for real-time cloud resource management.
- Own features from design through implementation, testing, and production rollout.
- Debug complex production issues across cloud providers, Kubernetes clusters, and distributed services.
- Collaborate with product and engineering teams to solve problems without textbook solutions.
- Work with time-series data, cloud provider APIs, and Kubernetes control plane internals.
- Contribute to product direction and backlog.
- Participate in on-call rotation for teams that require it.
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