AI Platform Engineer

DAMAC AI is running NVIDIA NCP-compliant GPU infrastructure — B300/GB300 Blackwell clusters. Someone needs to build the platform layer that turns that raw compute into something data scientists and ML engineers can actually use. That's this role.The RoleYou will design and build the orchestration, scheduling, and tooling layer that sits between GPU hardware and AI workloads — Kubernetes-based GPU orchestration, MLOps enablement, platform observability, and developer experience. This is greenfield platform engineering at the hardware-to-workload boundary.What You Will DoDesign AI platform architecture bridging GPU hardware and workload layersBuild Kubernetes-based GPU orchestration — scheduling, resource allocation, multi-tenancyBuild self-service MLOps tooling for data scientists and ML engineersOwn platform observability and performance monitoringImplement platform security and compliance for multi-tenant environmentsDrive automation and developer experience improvementsWhat You BringStrong Kubernetes experience with GPU orchestration — device plugins, MIG, time-slicingPlatform or MLOps tooling experience abstracting infrastructure for ML usersDirect NVIDIA GPU infrastructure experience — DGX/HGX or NCP-compliant environmentsPlatform architecture experience at the hardware-to-workload boundaryStrong automation and CI/CD skillsComfortable building from scratch in a fast-moving environmentDubai-based. Immediate requirement.