Staff Site Reliability Engineer, Infrastructure Engineering

Job summary

Manage and develop the internal platform for AI/ML at Tes***, focusing on Kubernetes, GPU infrastructure, and modern ML platforms.

Qualifications

• Built agent sandbox/ephemeral compute platforms
• Built GPU sandboxes with secure isolated environments
• Configured and troubleshot RoCE v2 / RDMA fabrics
• Production experience with KServe, Triton, vLLM, or Ray Serve
• Built or operated training-as-a-service and job scheduling
• Integrated platform services into an internal cloud
• Root cause analysis and systems thinking
• Deep Kubernetes internals expertise
• Built production Kubernetes operators in Go
• Production Cluster API experience

Responsibilities

• Build and own the end-to-end AI/ML platform as a self-service product for internal users
• Write production Kubernetes operators and controllers in Go for GPU workloads and cluster lifecycle
• Operate large-scale GPU fleets scheduling and health monitoring
• Own RoCE/RDMA networking for distributed training
• Build and operate inference infrastructure using KServe, Triton, and Ray Serve
• Build and operate training-as-a-service and distributed training
• Design secure, isolated sandbox environments for AI agents
• Architect serverless ephemeral workloads and event-driven compute
• Integrate GPU compute and sandboxes as services in the internal cloud platform
• Manage Kubernetes cluster lifecycle at fleet scale using Cluster API

Skills

Relocation