Senior Kubernetes Engineer

We are looking for a Senior Kubernetes Engineer to join our team and take ownership of key areas within our technology and data platform. Owns Kubernetes on client owned infrastructure: bare metal, private cloud, and sovereign environments. Designs the reference deployment that a client operations team can install and run.

Key Responsibilities

• Own and deliver solutions within the scope of the role, from requirements and technical/design decisions through implementation and continuous improvement.

• Work closely with engineering, product, data, design, and business stakeholders to translate requirements into practical, scalable solutions.

• Apply strong engineering and/or domain expertise to build reliable, maintainable, and production-ready capabilities.

• Contribute to architecture, standards, documentation, quality, and technical decision-making appropriate to the role.

• Identify performance, scalability, data quality, usability, reliability, or operational risks and address them proactively.

• Collaborate across teams to ensure solutions integrate effectively with existing systems and platform components.

• Experience: 7+ years of relevant professional experience.

• Strong hands-on experience with: Kubernetes on bare metal, CNI, CSI and persistent storage, ingress and load balancing, etcd operations, Helm, ArgoCD or Flux, Terraform, air gapped deployment.

• Built and operated production clusters on bare metal or private cloud, not only EKS, AKS, or GKE.

• Deep on what managed services abstract away: CNI behaviour, persistent storage, ingress and load balancing without a cloud provider, PKI and certificates, etcd backup and recovery.

• Air gapped or restricted network deployment, including image mirroring and offline installation.

• Runs stateful data workloads on Kubernetes: Kafka, Flink, Spark, databases, and their operators.

• Hardening in regulated environments: policy enforcement, network policy, secrets management, RBAC design.

• Capacity planning, high availability, disaster recovery, and zero downtime upgrades.

Preferred Qualifications

• GPU scheduling and node management for AI workloads.

• On premises deployment in telco or banking environments.

• Experience in client data centres and security reviews.