Senior Systems Architect

3 days ago

bangalore, India beBeeSystem Full time

We are looking for a talented Senior System Engineer to join our team.About the RoleAs a Senior System Engineer, you will be responsible for designing and implementing custom Kubernetes controllers that manage GPU node lifecycle across multiple cloud providers. Your goal will be to handle provisioning, bootstrapping, and deprovisioning with zero manual intervention.Duties and ResponsibilitiesDesign and implement custom Kubernetes controllers to manage GPU node lifecycleBuild and maintain autoscaling systems that analyze pod resource requirements (GPU count, memory, CPU) and automatically provision appropriate instance types from provider APIsDevelop microVM-based isolation for multi-tenant GPU workloads using cloud-hypervisor with VFIO GPU passthrough, enabling secure, high-performance compute for inference and training workloadsCreate node bootstrapping automation that provisions bare-metal or cloud instances with container runtimes, GPU drivers, Kubernetes components, and custom networking configurations via cloud-init, Ansible, or TerraformImplement sophisticated networking solutions connecting hybrid infrastructure - GKE/GCE control planes with external GPU workers using WireGuard, custom CNI configurations, and cross-cloud service meshBuild Kubernetes operators and CRDs for managing ML infrastructure components like model registries, inference endpoints, training job orchestration, and GPU time-slicing configurationsDesign monitoring, cost optimization, and capacity planning systems that provide visibility into GPU utilization, workload patterns, and infrastructure efficiency across heterogeneous compute poolsThis role requires strong proficiency in Go and experience building scalable, reliable services that manage complex distributed systems. You should also have hands-on experience with GPU infrastructure in Kubernetes - NVIDIA GPU Operator, device plugins, time-slicing configurations, or custom GPU scheduling logic. Additionally, you should have deep understanding of Kubernetes architecture including admission controllers, scheduler extenders, resource lifecycle management, and cluster autoscaling mechanisms.The ideal candidate will have 5+ years of experience building production Kubernetes systems and demonstrated ability to design and implement automation systems that replace manual processes with API-driven, self-service tooling. Experience with at least one cloud provider's APIs (GCP, AWS, Azure) for programmatic compute provisioning and management is also required. Strong Linux systems knowledge including networking, storage, and virtualization is essential for this role.

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