Senior Security Engineer, AI Architecture Patterns

• Develop shift-left solutions that embed security standards directly into AI-generated code, ensuring constraints, scaffolds, rules, and guardrails are applied during the coding process rather than post-shipment.
• Convert written security standards into machine-enforceable formats, enabling programming assistants and pipelines to apply them seamlessly without manual interpretation.
• Engineer verification workflows that confirm generated code meets claimed standards, providing evidence for downstream release gating and policy enforcement.
• Maintain and evolve evaluation harnesses, reference repositories, known-bad cases, and encouraged findings to ensure constraint-steered agents produce conformant code amid changing models, patterns, and tooling.
• Convert approved architecture patterns into secure, runnable reference implementations, such as templates, Helm charts, container images, and worked examples, ensuring their reliability as platforms evolve.
• Elevate AI solutions from prototypes to enterprise-grade products, focusing on versioning, observability, latency and cost budgets, access control, audit, graceful failure, and a detailed support and deprecation model.
• Set the enterprise-grade architecture standards for AI tooling in a security context, determining qualification criteria and monitoring adoption and outcomes to ensure efficiency.
• Collaborate with teams developing their own AI and automation solutions, reviewing builds, sharing platforms, raising engineering standards, and staying engaged through adoption, validation, and residual-risk closure.
• 8+ years in security engineering, platform engineering, product security, infrastructure security, DevSecOps, developer experience engineering, or software engineering for large distributed systems.
• Proven skill in building evaluation harnesses and regression suites for non-deterministic systems, including reference cases, result verification, conformance measurement, and regression detection.
• Proficiency in cloud-native and containerized environments, including Kubernetes, Helm, container image hardening, and the composition of identity, networking, and infrastructure primitives into secure systems.
• Proven track record of taking systems from prototype to production at enterprise standards, including reliability, versioning, observability, access control, documentation, and support models.
• Ability to operate as both an architect and engineer, reviewing builds, explaining technical trade-offs, influencing without direct authority, and improving team capabilities.
• BS/MS in Computer Science, Engineering, or equivalent experience.
• Experience in shipping tooling that steers or constrains code-generating agents at generation time.
• Providing attestation, provenance, or supply chain evidence for downstream use, including signing, verifiable claims, or build-time evidence.
• Productizing internally built AI tooling, taking demos to enterprise-grade solutions with evaluation, guardrails, cost controls, and operational support.
• Establishing a golden-path, paved-road, or internal developer platform program at scale, including content models, tooling, and adoption strategies.
• Engagement in ongoing technical development or knowledge sharing to broaden risk reduction strategies, through open-source work, technical writing, standards work, conference talks, mentoring, or community participation.
• You will also be eligible for equity and benefits .
• Applications for this job will be accepted at least until October 20, 2026.
• This posting is for an existing vacancy.
• NVIDIA uses AI tools in its recruiting processes.

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