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NVIDIA

Senior Solutions Architect, Agentic AI — Safety and Security

  • Remote
  • 6+ years

Salary

$184,000 - 287,500/ year

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Description

We are looking for a Senior Solutions Architect to help leading Enterprise ISVs design, build, and deploy secure agentic AI systems on NVIDIA’s accelerated computing platform.

In this role, we will partner with strategic software companies across cybersecurity, AI safety, infrastructure protection, and confidential computing. Together, we will help them build trustworthy AI products that meet enterprise expectations for security, privacy, safety, reliability, and performance.

This work includes multi-agent orchestration, guardrails, agent runtime security, RAG, tool use, model customization, policy enforcement, OpenShell-like execution environments, and confidential AI deployments on protected infrastructure.

What you'll be doing:

Lead strategic agentic AI partner engagements from discovery and architecture through PoC, production readiness, rollout, and scale.

Build enterprise-grade agentic AI systems with multi-agent workflows, tool-using agents, RAG, planning, memory, evaluation, guardrails, policy enforcement, and failure containment.

Partner with security ISVs to integrate NVIDIA models into detection and response products, including threat triage, investigation agents, remediation workflows, natural-language-to-query, analyst automation, PII handling, and content safety.

Architect secure and confidential AI deployments using NVIDIA Confidential Computing, GPU attestation, KMS integration, protected infrastructure, air-gapped patterns, and partner key-management workflows.

Create PoCs, benchmarks, reference architectures, reusable blueprints, field guidance, and product feedback that help NVIDIA and our partners move secure AI systems into production.

What we need to see:

BS, MS, or PhD in Computer Science, Electrical Engineering, AI/ML, or equivalent experience

8+ years in engineering, solutions architecture, applied ML, enterprise software, or technical deployment.

Experience leading AI, ML, distributed systems, or enterprise software projects from prototype to production.

Hands-on experience building LLM, generative AI, RAG, or agentic AI applications in production or production-like environments.

Depth in one or more areas such as AI/LLM security, enterprise cybersecurity, trust and safety, confidential computing, secure AI infrastructure, model customization, post-training, or model evaluation.

Strong Python and Linux skills, experience with PyTorch, TensorFlow, or similar frameworks, and working knowledge of risks such as prompt injection, jailbreaks, tool-based data exfiltration, unsafe tool invocation, and model or skill supply-chain risk.

Ways to stand out from the crowd:

Experience with NVIDIA AI software such as NIM, NeMo Framework, NeMo Retriever, NeMo Guardrails, NeMo Agent Toolkit, Dynamo, Nemotron, Nemotron Safety models, Triton, TensorRT-LLM, or NIM Operator.

Experience with LLM red-teaming, AI safety evaluation, adversarial testing, prompt-injection defense, policy enforcement, Garak, NeMo Auditor, or release-gating evaluation benchmarks.

Experience with OpenShell, agent harnesses, sandboxed execution, secure tool invocation, agent runtime security, AI/software supply-chain security, model or skill signing, provenance, attestation, VEX, or secure model registries.

Experience building post-training pipelines or GPU-accelerated safety and security workflows, including reasoning, tool use, domain adaptation, safety alignment, PII/NER detection, content-safety models, TensorRT optimization, quantization, workshops, architecture reviews, whitepapers, or reference architectures.

Experience with confidential computing, including GPU confidential computing, remote attestation, Confidential Containers, enterprise KMS, air-gapped deployments, AMD SEV-SNP, or Intel TDX.

With competitive salaries and a generous benefits package, NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. This role presents an opportunity to have a wide impact at NVIDIA by improving the factory planning function. Are you creative, hard-working, dedicated, and determined? Do you love a challenge? If so, we want to hear from you!

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD.

Responsibilities

  • Applications for this job will be accepted at least until September 1, 2026.
  • This posting is for an existing vacancy.
  • NVIDIA uses AI tools in its recruiting processes.
  • NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Where you’d work

Fully remote

You can work from

  • United States

About the company

NVIDIA

Also hiring in United States, Munich, Germany, France and 14 more places

273 of their 660 open roles are remote

Your chances

Worth a look before you spend an evening tailoring a CV for it.

  • 21 checks run
  • 2 red flags

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14 checks

1 red flag

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  • Must-haves
Details12 facts · Role, Location, Compensation, Employment
Tech stack
  • Python
  • Linux
  • LLMs
Seniority
Senior
Type
Full-time
Equity
Equity offered
Specialty
Solutions Engineering
Region
United States
Show 6 more factsShow less

Role

Category
Solutions & Support
Specialty
Solutions Engineering
Seniority
Senior
Experience
8+ years
Tech stack
  • Python
  • Linux
  • LLMs

Location

Work model
Remote
Region
United States
Remote from
  • United States

Compensation

Salary
$184,000 - 287,500 / year
Pay period
Annual
Equity
Equity offered

Employment

Type
Full-time

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