Platform Engineer
TypeFull-time job
LocationAustralia
Posted3 hours ago
What You'll Be Doing
Build and run a multi-tenant GPU-as-a-Service platform for AI workloads
Develop GPU orchestration and scheduling using Kubernetes and Slurm
Manage GPU-enabled Kubernetes clusters with advanced workload scheduling
Write Infrastructure-as-Code with Terraform and Ansible
Deliver self-service APIs, CLI tools and portals for GPU provisioning
Create CI/CD pipelines for platform services, backed by strong monitoring and observability
Work with teams across AI training and inference
Improve GPU utilisation, performance and cost efficiency
Own reliability, security and tenant isolation, including incident response and ongoing improvement
Looking for
4-8+ years in platform engineering or SRE
Deep experience with Kubernetes and cloud-native infrastructure at scale
Background in GPU workloads, distributed systems, CI/CD and automation
Strong communication and stakeholder skills
Comfortable juggling priorities in a fast-moving environment
Proactive, collaborative and solutions-focused
Sharp focus on performance, cost, security and operational detail
Bonus Points
GPUaaS or HPC platform experience
Familiarity with PyTorch, TensorFlow, Ray, Kubeflow or Airflow
Knowledge of CUDA and NCCL; cloud certifications are a plus
Originally posted on Himalayas
Build and run a multi-tenant GPU-as-a-Service platform for AI workloads
Develop GPU orchestration and scheduling using Kubernetes and Slurm
Manage GPU-enabled Kubernetes clusters with advanced workload scheduling
Write Infrastructure-as-Code with Terraform and Ansible
Deliver self-service APIs, CLI tools and portals for GPU provisioning
Create CI/CD pipelines for platform services, backed by strong monitoring and observability
Work with teams across AI training and inference
Improve GPU utilisation, performance and cost efficiency
Own reliability, security and tenant isolation, including incident response and ongoing improvement
Looking for
4-8+ years in platform engineering or SRE
Deep experience with Kubernetes and cloud-native infrastructure at scale
Background in GPU workloads, distributed systems, CI/CD and automation
Strong communication and stakeholder skills
Comfortable juggling priorities in a fast-moving environment
Proactive, collaborative and solutions-focused
Sharp focus on performance, cost, security and operational detail
Bonus Points
GPUaaS or HPC platform experience
Familiarity with PyTorch, TensorFlow, Ray, Kubeflow or Airflow
Knowledge of CUDA and NCCL; cloud certifications are a plus
Originally posted on Himalayas
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