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 robust 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