Platform Engineer

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