• Get AI Platform customers production-ready on Hydra — standing up Kubernetes clusters, configuring GPU drivers, validating networking, and troubleshooting the issues that surface when real workloads hit real hardware. • Own the bare metal ←→ platform layer — bridging GPU infrastructure (NCCL, InfiniBand, NVLink, storage) with orchestration layers (Kubernetes, SLURM) and MLOps tooling that customers actually use. • Configure, benchmark, and debug NVIDIA driver stacks — firmware versions, CUDA compatibility, NCCL tuning, MIG configurations. • Run quality benchmarks and diagnostics to validate performance for inference and training workloads across chip types. • Identify gaps before customers do — pressure-testing Hydra's infrastructure, APIs, and workflows to find what's missing or broken. • Turn customer learnings into product — working with Product and Engineering to build reusable templates, default configurations, and automated workflows that eliminate manual onboarding. • Advise customers on chip selection and tokenomics — helping AI platform customers understand price/performance trade-offs across GPU types, cost-per-token economics, and which hardware fits their inference or training workloads.