Senior Engineer, NCX

NVIDIA

Senior Engineer improving NVIDIA Cloud Partner infrastructure operations for AI computing environments. Building observability, automation, lifecycle management, and operational readiness for large-scale GPU clusters.

Posted 9/1/2026full-timeRemote • 🇩🇪 GermanySenior💰 PLN 292,500 - PLN 650,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in managing NVIDIA accelerated infrastructure, including automation, operational readiness, and lifecycle management for large-scale AI clusters. Proficient in developing observability metrics, health signals, and operational standards to ensure infrastructure reliability and service readiness.

Highest-signal resume keywords

Infrastructure EngineeringKubernetes ManagementAutomation DevelopmentNVIDIA TechnologiesObservability Tools

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Hard Skills

Linux-Based Distributed SystemsCloud InfrastructurePython ProgrammingGo ProgrammingShell ScriptingGPU Lifecycle ManagementConfiguration ManagementFailure DetectionNetworking FundamentalsAI Workload Management

Tools & Technologies

PrometheusGrafanaOpenTelemetryAlertmanagerNVIDIA DGX SystemsInfiniBandRoCEKubernetesTelemetry PipelinesOperational Dashboards

Industry Keywords

Site Reliability EngineeringDevOpsCloud Platform EngineeringProduction EnvironmentsService Level AgreementsOperational SignalsAI TrainingInfrastructure ObservabilityNVIDIA Cloud PartnersHyperscale Cloud Providers

Tech Stack

Tools & technologies

CloudDistributed SystemsGoGrafanaKubernetesLinuxNode.jsPrometheusPythonShell Scripting

About the role

Key responsibilities & impact

Lead NVIDIA Cloud Partner Day 2 operational readiness efforts.
Collaborate with NVIDIA Cloud Partners to establish systems, procedures, automation, and operational methods for managing NVIDIA accelerated infrastructure after deployment and activation.
Develop continuous validation methods for GPU, CPU, storage, and network health across large-scale AI clusters.
Establish telemetry, monitoring, alerting, dashboards, and operational signals across compute, GPU, InfiniBand/RoCE networking, storage, Kubernetes, and AI workloads.
Build automated workflows to detect, isolate, drain, repair, validate, and return unhealthy infrastructure to service.
Implement scalable GPU fleet lifecycle strategies, including driver and firmware administration, Kubernetes node maintenance, OS patching, configuration management, upgrades, and configuration drift identification.
Translate NVIDIA NCP requirements and reference architectures into production practices, validation criteria, runbooks, automation, and measurable operational standards.
Develop health signals, SLOs, metrics, acceptance criteria, and ongoing validation mechanisms for infrastructure reliability and service readiness.
Develop reusable tooling, automation, implementation guides, runbooks, operational playbooks, and reference implementations across multiple NCP environments.

Requirements

What you’ll need

BS, MS, or Ph.D. in Computer Science, Computer/Electrical Engineering, or a related technical field, or equivalent experience.
8+ years of experience in infrastructure engineering, Site Reliability Engineering, DevOps, cloud platform engineering, systems engineering, or similar roles supporting large-scale production environments.
Strong experience operating Linux-based distributed systems and cloud infrastructure in production.
Deep understanding of Kubernetes, containers, cluster scheduling, and the operational lifecycle of large multi-node environments.
Strong understanding of production observability, including metrics, logging, alerting, dashboards, health checks, and operations guided by service level agreements.
Experience crafting automation for infrastructure lifecycle management, failure detection, remediation, upgrades, and configuration management.
Strong networking fundamentals and experience troubleshooting complex distributed systems across compute, network, and storage layers.
Programming and automation experience using Python, Go, shell scripting, or similar languages.
Experience managing extensive GPU or accelerated computing infrastructure that supports AI training and inference workloads.
Experience with NVIDIA technologies including DGX/HGX systems, CUDA, NVLink/NVSwitch, NVIDIA networking, InfiniBand, RoCE, GPU Operator, Network Operator, or related NVIDIA infrastructure software.
Proven experience collaborating with NVIDIA Cloud Partners, hyperscale cloud providers, managed AI clouds, or extensive service-provider infrastructure and operating SLOs for large-scale compute infrastructure and using operational data to improve availability, performance, and fleet efficiency.
Extensive knowledge of infrastructure observability tools including Prometheus, Grafana, OpenTelemetry, Alertmanager, and scalable telemetry pipelines.
Knowledge of failure modes related to large distributed AI workloads and the infrastructure features necessary to consistently support extended training and production inference.