Sr GPU Cloud K8S Expert (SRE SME)

Bitdeer is a world-leading technology company for AI and Bitcoin mining infrastructure.

Bitdeer is committed to providing comprehensive Bitcoin mining solutions for its customers and building AI computational infrastructure to support the AI revolution. Bitdeer handles complex processes involved in computing such as equipment procurement, transport logistics, data center design and construction, equipment management, and daily operations. Bitdeer also offers advanced cloud capabilities to customers with high demand for artificial intelligence.

Headquartered in Singapore, Bitdeer has deployed data centers across multiple countries, including the United States, Norway, Bhutan, and Ethiopia.
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Position Overview

You run the control plane where AIOps meets tenants — where topology-aware scheduling, self-healing, and agent-driven remediation actually execute.

NeoCloud is building an AI-operated GPU cloud. Kubernetes is where all of that lands on real customer workloads: the topology-aware scheduler places jobs on the right NVLink domain, the operator drains and reschedules around predicted faults, and the tenant boundary is enforced against a Bare-Metal-as-a-Service backend. In this role you design, deploy, and operate that control plane — and you make sure the AIOps substrate can reach in and remediate without a human on the pager.

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.

Job Requirement:

• 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 workflows (ArgoCD/Flux)
• Strong SRE background: SLI/SLO frameworks, incident management, capacity planning
• Experience with Prometheus, Grafana, and alerting at scale
• Strong programming skills in Go or Python for operator/CRD development
• AIOps aptitude — you think of the K8S control plane as an execution surface for automated remediation, not just a scheduler. You've either wired an autoscaler/remediator loop into K8S or you can design one.
• Runbook-as-code mindset — every SRE playbook you write should be executable by the platform.

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