Sr. GPU Cloud K8S Expert (SRE SME)

About Bitdeer: Bitdeer is a world-leading technology company for Bitcoin mining and AI cloud. Bitdeer is committed to providing comprehensive Bitcoin mining solutions for its customers. Apart from designing industry-leading ASIC chips and manufacturing mining rigs, the Group handles complex processes involved in computing across the value chain. This includes equipment procurement, transport logistics, datacenter design and construction, equipment management, and network and facility operations. Bitdeer also offers advanced cloud capabilities to customers with a high demand for artificial intelligence. Headquartered in Singapore, Bitdeer operates globally with a diversified 3 GW energy portfolio, and deploys Bitcoin mining and HPC datacenters in the United States, Bhutan, Norway, Canada, Malaysia, and Ethiopia. What you will be responsible for: What you'll own Production Kubernetes clusters optimized for GPU workloads at scale (100–10,000 GPUs). Nvidia GPU operator, device plugin, MIG configuration, and GPU time-slicing policies. Topology-aware scheduling: GPU locality, NVLink domain awareness, network rail affinity. Custom Resource Definitions (CRDs) for GPU workload lifecycle management. AI framework integrations: Slurm on K8S, Ray on K8S, Kubeflow. Multi-tenant isolation: namespaces, network policies, resource quotas, RBAC, pod security standards. Bare-Metal as a Service (BMaaS): automated provisioning, tenant onboarding, lifecycle, reclamation. Terraform providers and modules for infrastructure-as-code across GPU clusters. SLIs/SLOs for cluster availability, job completion rates, and provisioning latency. Incident management: runbook automation, escalation, post-incident reviews. Monitoring stack: Prometheus, Grafana, Alertmanager, PagerDuty. GPU node failure handling: automated detection, drain/cordon/taint, workload rescheduling. Feed the AIOps substrate The remediation-actuator and workflow engine land here — you make the control plane safe for automated action. Your CRDs are the schema the platform's predictors and remediators write against. Every human intervention you do this quarter becomes an autonomous workflow next quarter. What success looks like in year 1 Automated drain/reschedule around predicted GPU faults, at scale, without customer impact. BMaaS live for external tenants with self-service onboarding. Cluster availability and job-completion SLOs published and met. How you will stand out: 5+ years in Kubernetes operations, with at least 2 years managing GPU workloads on K8S Deep understanding of Nvidia GPU operator, device plugin, and GPU scheduling in K8S Experience with topology-aware scheduling and GPU-specific resource management Hands-on experience building multi-tenant K8S platforms with strong isolation guarantees Experience with bare-metal server provisioning and lifecycle automation (Ironic, MAAS, or custom) Proficiency in Terraform, Helm, and GitOps…