Senior AI Platform Engineer - Kubernetes & GPU Cloud

Bitdeer (NASDAQ: BTDR); Singapore; On-site; SGD 180,000 - 260,000; Full time

Benefits offered by this job

Attractive welfare benefits

Career development opportunities

Hybrid/onsite options

Job summary

Bitdeer is seeking a Senior SRE/Platform Engineer to operate and harden Kubernetes-based MaaS production environments, define SLOs, and automate repetitive operations. You will work on GPU AI infrastructure, capacity planning, and incident response across regional cloud deployments.

You will collaborate with runtime and performance engineers to diagnose incidents from public API edge to model worker, contribute to rollout safety, and drive improvements with Helm/Argo CD and GitOps.

Qualifications

• 6+ years in SRE, platform or infra engineering.
• Deep Kubernetes experience with Helm, Argo CD and GitOps.
• Hands-on GPU/AI infra or HPC experience preferred.
• Strong observability with metrics and tracing.
• Proficient in Go, Python, Bash.

Responsibilities

• Operate and harden Kubernetes-based MaaS production environments across CPU platform nodes, edge ingress, and regional GPU tiers.
• Define and own SLOs, alerting, dashboards, runbooks, and incident response for API availability, latency, error rate, capacity, and GPU health.
• Improve rollout safety for model/runtime/platform changes using canaries, fallback, health‑aware routing, maintenance mode, and fast rollback.
• Drive capacity planning for GPU utilization, burst traffic, quota/rate limits, cross-region latency, and customer growth.
• Automate repetitive operations through Helm, Argo CD, operators, scripts, and self-healing workflows.
• Partner with runtime and performance engineers to debug incidents from public API edge to model worker.

Skills

Kubernetes expertise

Go/Python scripting

Cloud infrastructure

Observability

Tools

Prometheus

OpenTelemetry

VictoriaMetrics

Job description

Bitdeer is seeking a Senior SRE/Platform Engineer to operate and harden Kubernetes-based MaaS production environments, define SLOs, and automate repetitive operations. You will work on GPU AI infrastructure, capacity planning, and incident response across regional cloud deployments.

You will collaborate with runtime and performance engineers to diagnose incidents from public API edge to model worker, contribute to rollout safety, and drive improvements with Helm/Argo CD and GitOps.