Senior Data Cloud Engineer Jobs

NVIDIA is hiring an NCX Senior Engineer who is passionate about NVIDIA Cloud Partner (NCP) infrastructure operations to join our DSX team. This role involves working closely with strategic NVIDIA Cloud Partners to build and improve the operational capabilities essential for running large-scale NVIDIA accelerated infrastructure reliably in production. Your role involves guiding partners beyond the initial cluster deployment and validation phase into advanced Day 2 operations. These operations cover ongoing infrastructure health, observability, lifecycle management, quick remediation, performance validation, and operational readiness. You will engage directly with partner engineering and operations teams to develop consistent approaches that support NVIDIA workloads and the broader external customer environments of the partners. This is a highly technical, hands-on role at the intersection of NVIDIA accelerated computing, cloud infrastructure, distributed systems, and production operations. What you'll be doing: Lead NCP Day 2 operational readiness efforts. Collaborate directly with NVIDIA Cloud Partners to set up the systems, procedures, automation, and operational methods necessary to consistently manage NVIDIA accelerated infrastructure following initial deployment and activation. Build continuous infrastructure validation. Develop and implement methods to continuously validate GPU, CPU, storage, and network health. Do this across large-scale AI clusters to identify degraded infrastructure before it impacts critical training or inference workloads. Establish observability and operational telemetry. Help NCPs implement comprehensive telemetry, monitoring, alerting, dashboards, and operational signals across compute, GPU, InfiniBand/RoCE networking, storage, Kubernetes, and AI workloads. Devel

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About the Role REMOTE IN INDIA We're looking for a software engineer to build the Kubernetes-native control plane that provisions and runs our GPU inference fleet. You'll design a manifest-driven API where the inference team declares what they need, whether that's a cluster, a model deployment, or a capacity change, and our controllers handle the reconciliation, provider/runtime selection, and lifecycle management underneath, so the inference team never has to know or care which specific serving stack, scheduler, or hardware pool is doing the work. You'll also build the systems that keep the fleet efficient, not just running, including defragmentation and rebalancing logic that consolidates scattered workloads back into contiguous capacity, and scheduling/bin-packing improvements that push GPU utilization up without hurting latency. The core value we're after is decoupling the people building on top of the platform from the operational and runtime complexity underneath, while squeezing more usable capacity out of the same hardware. You'll build the controllers, reconciliation loops, and self-service surface (API/CLI, not tickets) that make that decoupling real, plus the event-driven health, remediation, and utilization systems that keep it running and efficient without a human in the loop. Strong candidates have hands-on experience with Kubernetes controller/CRD patterns, have built or operated a platform API that abstracts multiple backends behind one interface, understand GPU scheduling and capacity efficiency (fragmentation, bin-packing, right-sizing), and think about GPU infrastructure as software to be engineered. A product mindset - you've built internal platforms or APIs consumed by other engineering teams and care about the developer experience of what you ship. You build it, you own it. You are not only responsible for delivering the software but also for operating and supporting it in production. Responsibilities Build the provisioning state machine

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