Head of AI Infrastructure Security

About Nava

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.

About the Role

The Infrastructure Security team owns security architecture, engineering and operational controls across Nava's GPU clusters, networks, management planes, Kubernetes and AI inference infrastructure.

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.

What You’ll Do

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Define secure-by-design baseline for Linux, firmware, BMC, GPU drivers, orchestration and infrastructure APIs.

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Architect IAM/PAM, secrets, PKI, encryption, segmentation and dedicated workload isolation.

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Secure Kubernetes, containers, infrastructure-as-code, deployment pipelines and software supply chain.

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Build vulnerability discovery, remediation SLAs, security telemetry, incident response and threat modeling.

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Integrate security acceptance checks into commissioning deployment and post-handover operations.

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Lead security engineers and support enterprise assurance, regulatory and customer reviews.

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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.

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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).

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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.

What You’ll Need

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10–15 years of hands-on infrastructure/platform security with engineering leadership.

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Strong Linux, Kubernetes, networking, IAM and cryptographic controls knowledge.

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Experience protecting management planes, privileged access and multi-tenant services.

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Security automation and detection engineering skills; credible incident leadership.

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Understanding of ISO 27001, SOC 2, CIS and relevant customer/regulatory obligations.

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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.

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AI/HPC or GPU-specific infrastructure attack surfaces and workload isolation.

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Experience with cloud platforms, network infrastructure or security-focused tech environments.