<div>Nava is a neocloud company purpose-built for the AI era. We design, deploy, and operate large-scale GPU infrastructure and deliver inference-as-a-service to teams building the next generation of AI products. Our platform runs on NVIDIA GPU systems, high-performance RDMA fabrics, and a fully automated, software-defined operations model. Every engineer at Nava works close to the metal, on infrastructure built to keep GPUs saturated and models serving.</div><div> </div>

Nava

About Nava&nbsp;&nbsp;
Nava is a neocloud company purpose-built for the AI era. We design, deploy, and operate large-scale GPU infrastructure and deliver inference-as-a-service to teams building the next generation of AI products. Our platform runs on NVIDIA GPU systems, high-performance RDMA fabrics, and a fully automated, software-defined operations model. Every engineer at Nava works close to the metal, on infrastructure built to keep GPUs saturated and models serving.&nbsp;&nbsp;
About the Role&nbsp;
The Infrastructure Security team owns security architecture, engineering and operational controls across Nava's GPU clusters, networks, management planes, Kubernetes and AI inference infrastructure.&nbsp;
Own end-to-end security architecture and operations across GPU infrastructure. Lead platform hardening, network segmentation, IAM, workload isolation, vulnerability management, compliance automation, threat detection, incident response, and customer data protection.&nbsp;
What You’ll Do&nbsp;
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Define secure-by-design baseline for Linux, firmware, BMC, GPU drivers, orchestration and infrastructure APIs.&nbsp;
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Architect IAM/PAM, secrets, PKI, encryption, segmentation and dedicated workload isolation.&nbsp;
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Secure Kubernetes, containers, infrastructure-as-code, deployment pipelines and software supply chain.&nbsp;
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Build vulnerability discovery, remediation SLAs, security telemetry, incident response and threat modeling.&nbsp;
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Integrate security acceptance checks into commissioning deployment and post-handover operations.&nbsp;
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Lead security engineers and support enterprise assurance, regulatory and customer reviews.&nbsp;
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Own the AI-datacenter attack surface specific to GPU clusters: multi-tenant GPU isolation (MIG / time-slicing boundaries), confidential computing and attestation for tenant workloads, and protection of NVLink/NVSwitch and RDMA (InfiniBand/RoCEv2) fabric segmentation.&nbsp;
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Harden the out-of-band and firmware supply chain — BMC/Redfish/IPMI, UEFI/BIOS, GPU and DPU (BlueField) firmware, UFM/NetQ fabric managers — with signed-image enforcement and secure provisioning (PXE/Redfish).&nbsp;
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Secure the AI/ML plane: model, dataset and checkpoint confidentiality on shared storage, inference-endpoint and API protection, and tenant data isolation across the GPU fabric.&nbsp;
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What You’ll Need&nbsp;
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10–15 years of hands-on infrastructure/platform security with engineering leadership.&nbsp;
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Strong Linux, Kubernetes, networking, IAM and cryptographic controls knowledge.&nbsp;
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Experience protecting management planes, privileged access and multi-tenant services.&nbsp;
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Security automation and detection engineering skills; credible incident leadership.&nbsp;
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Understanding of ISO 27001, SOC 2, CIS and relevant customer/regulatory obligations.&nbsp;
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Within that, hands-on GPU / AI-datacenter exposure is what we weigh most — we value demonstrated depth over years, as the field is still emerging.&nbsp;
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AI/HPC or GPU-specific infrastructure attack surfaces and workload isolation.&nbsp;
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Experience with cloud platforms, network infrastructure or security-focused tech environments.
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