Senior Solutions Architect – AI Infrastructure Security (remote on the East coast)

Description

The team is hiring a senior solutions architect with deep security expertise to join the Voyager team in the CTO's office. You will own security design for the company's AI platform, which builds and runs GPU clouds and AI factories from bare metal up to models.

You will set out how environments on the platform are secured across every layer: the firmware and hardware they boot from, the hosts, VMs and Kubernetes clusters they run, the networks and shared storage, the secrets and certificates that connect them, and the AI models and applications they serve. Your designs will become published reference architectures, working code and proofs of concept running on real hardware with customers and partners.

Research is central to the role. You will look into threats, designs and technologies before customers raise them, build prototypes that may never ship, and push back on accepted practice when a better approach exists. Your work will shape where the platform goes next, not only how it is deployed today.

The job mixes research with hands-on, outward-facing work: exploring designs, writing architecture, building and validating it in the lab, and explaining it to engineers, security teams, executives and conference audiences. You will work with solutions architects, partner management and engineering colleagues across many countries and time zones.

The team is building the observability foundation for the AI cloud era with GPU cloud operators, neoclouds, sovereign clouds and AI-first enterprises. You will help shape the product story and results in go-to-market.

The company is a Kubernetes-native AI infrastructure company, part of IREN. It helps organizations run scalable, secure and sovereign infrastructure for AI and data-heavy workloads on-premises, in the cloud, at the edge or in sovereign data centers.

Responsibilities

• Design and publish security reference architectures and solution designs for single-tenant private AI clouds, multi-tenant GPU clouds and hybrid setups spanning public cloud and on-premises.
• Set a defence-in-depth model for every layer, from hardware root of trust and firmware up through hosts, VMs, Kubernetes, networking, storage, models and applications.
• Define tenant isolation boundaries and their guarantees, recording where each is strong or weak and what it costs to strengthen.
• Map designs to the controls and frameworks customers must meet, and document trade-offs across risk, cost, performance and operability with reasoning customers can follow.
• Secure bare-metal hosts: secure and measured boot, TPM attestation, BMC and Redfish hardening, firmware integrity and hardened Linux.
• Secure virtualisation: hypervisor and KubeVirt hardening, VM isolation, and the security impact of PCI passthrough and SR-IOV for GPUs and NICs.
• Secure Kubernetes: RBAC, Pod Security Standards, admission control and policy-as-code, runtime security, and safe multi-cluster and multi-tenant operation.
• Design secrets management: secret stores, KMS and HSM integration, envelope encryption, secret delivery to workloads and rotation.
• Design certificates and PKI: internal certificate authorities, automated issuance and rotation, mTLS between services and workload identity.
• Design network security: segmentation, zero-trust models, Kubernetes network policy, tenant isolation on front-end and GPU fabrics, and DPU-based enforcement.
• Design storage security: encryption at rest and in transit, key management, and tenant isolation on shared block, file and object storage holding training data and model weights.
• Cover public cloud security: secure landing zones, identity and access management, network controls on major clouds, and how these extend into hybrid deployments.
• Define lifecycle protection for Transformer-based models: provenance and signing of weights, integrity of training and fine-tuning data, and secure model registries.
• Protect inference services and AI applications against prompt injection, data and model exfiltration, model theft and misuse of agentic tool access.
• Design isolation for shared GPU infrastructure (MIG, vGPU, passthrough) and assess confidential computing for protecting models and data in use.
• Secure the supply chain for platforms, containers and models: image signing, SBOMs, vulnerability management and provenance.
• Track and evaluate emerging security technologies, standards and threats, such as GPU confidential computing, remote attestation, post-quantum cryptography, model signing and AI agent security.
• Prototype alternative designs in the lab, including ones customers have not asked for, and benchmark them against current practice.
• Challenge established or customer-preferred designs when evidence points to a better option, and make the case with data.
• Publish research notes and design proposals internally, and where suitable, external papers, blog posts and talks.
• Build and run proofs of concept with customers and partners in lab and on customer sites, and report results against agreed success criteria.
• Write automation, policies and tooling (Python, Go, Bash, Ansible, Helm, Kubernetes manifests, Terraform, policy-as-code) so reference architectures can be reproduced.
• Assess designs and deployments through threat modelling, configuration review and hands-on testing, and turn the findings into design guidance.
• Act as the security subject matter expert in customer discovery, design reviews and architecture workshops, bringing new ideas rather than only restating current practice.
• Engage with customer security, risk and compliance teams, and support security questionnaires and architecture reviews for strategic deals.
• Work with hardware, software and security partners (e.g. NVIDIA, server OEMs, security vendors) on joint designs and validation.
• Feed research results back to Product, Engineering and the security team, and help shape the platform roadmap.
• Present at industry events, webinars and partner summits, and run hands-on workshops for technical audiences.
• Write technical content: reference architecture documents, solution briefs, blog posts and internal enablement material.

Requirements

• A bachelor's degree in computer science, computer engineering, cybersecurity or a related field, or equivalent practical experience.
• 8+ years in security engineering or security architecture, with at least 3 years securing cloud, Kubernetes or large-scale infrastructure platforms.
• Customer-facing experience as a solutions architect, pre-sales engineer, consultant or technical lead.
• Cloud security: you have designed and operated secure deployments on at least one major public cloud (AWS, Azure or GCP) and on private cloud or bare-metal infrastructure.
• Host and virtualisation security: Linux hardening (e.g. SELinux or AppArmor, CIS benchmarks), secure boot, TPM, and hypervisor and VM isolation including KubeVirt.
• Kubernetes security: RBAC, Pod Security Standards, admission controllers and policy engines (e.g. Kyverno, OPA Gatekeeper), runtime security (e.g. Falco) and multi-tenancy patterns.
• Secrets and key management: tools such as HashiCorp Vault or OpenBao, External Secrets Operator, cloud KMS and HSMs.
• PKI and identity: certificate lifecycle with tools such as cert-manager, mTLS, OIDC and SSO, and workload identity (e.g. SPIFFE/SPIRE).
• Network security: segmentation, firewalling, zero-trust architectures and Kubernetes network policy (e.g. Cilium, Calico).
• Storage and data security: encryption at rest and in transit, key hierarchy design and access control on shared storage.
• AI security: a working understanding of how Transformer-based models are trained, packaged and served, and of the threats specific to them (e.g. OWASP Top 10 for LLM Applications, MITRE ATLAS).
• Software supply chain security: image signing (e.g. Sigstore/cosign), SBOMs, vulnerability scanning and SLSA concepts.
• Programming or scripting in at least one language, Python or Go preferred, and comfort with Git, CI and infrastructure-as-code.
• Curious: you dig into how attacks really work, read specs, advisories and source code, follow how AI and infrastructure threats change, and question established designs, including ones customers rely on.
• Ownership: you take a reference architecture or proof of concept from first sketch to a published, validated result, and stand behind it.
• Proactive: you spot risks and gaps in the product, documentation or a customer design before being asked, and act on them.
• Fast learner: you become productive quickly with unfamiliar hardware, software or customer environments.
• Business acumen: you frame security decisions in terms of risk, cost, compliance and time-to-value, and explain them that way to decision makers.
• Excellent written and spoken English, and you write documents others can build from.
• Comfortable presenting to large audiences at conferences and events, and running hands-on workshops.
• Able to adapt your message from engineers to CISOs and customer executives.
• Experience working in an international, distributed company across time zones and work cultures, with solutions architects, partner management and engineering teams.
• Experience with compliance frameworks and benchmarks for cloud and AI infrastructure, such as NIST SP 800-53, NIST AI RMF, FedRAMP, SOC 2, ISO/IEC 27001, CIS benchmarks or DISA STIGs.
• Hands-on experience with confidential computing (e.g. AMD SEV-SNP, Intel TDX, NVIDIA GPU confidential computing) and remote attestation.
• Experience securing GPU cloud, neocloud or HPC environments, or NVIDIA Cloud Partner (NCP) style deployments.
• Experience with DPUs (e.g. NVIDIA BlueField) for infrastructure isolation and security offload.
• Offensive security or red-teaming experience, including against AI systems.
• Experience with the company products, including MKE, k0s and k0rdent security.
• Security certifications such as CISSP, CCSP, CKS, OSCP, or public cloud security specialty certifications.
• Contributions to open-source security or Kubernetes projects, or a record of public talks and technical writing.
• Participation in standards or industry bodies (e.g. CNCF TAG Security, OpenSSF, Confidential Computing Consortium, CoSAI), or published research, CVEs, patents or white papers.
• Additional languages beyond English.

Conditions

This role is fully remote and based in the United States, with a strong preference for candidates on the East Coast.

You will work with a team spread across many time zones, so some meetings will fall outside standard local hours.

Expect travel of up to 25% for customer engagements, partner meetings, lab work and industry events, mainly within the US and EU.

Compensation and benefits follow the local market and the type of employment arrangement. The company is an equal opportunity employer and may use automated decision-making in employment decisions, which you can opt out of or appeal.