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 Originally posted on Himalayas