We are looking for a Senior Solutions Architect to help leading Enterprise ISVs design, build, optimize, and deploy production-grade Agentic AI systems on NVIDIA’s accelerated computing platform. In this role, you will partner with strategic software companies to translate frontier AI capabilities into reliable enterprise products, spanning multi-agent orchestration, RAG, tool use, model customization, and production deployment.
We work at the intersection of partner engineering, NVIDIA’s AI platform, and product development. You will lead sophisticated technical projects from initial exploration through architecture, prototyping, performance optimization, production rollout, and field enablement. Together, we will help partners adopt NVIDIA technologies and GPU-accelerated infrastructure to bring next-generation AI products to market.
What you'll be doing:
Lead technical delivery for strategic Agentic AI partner engagements from discovery and architecture through PoC, production readiness, rollout, and scale.
Design and build enterprise-grade agentic systems, including multi-agent workflows, tool-using agents, RAG-integrated applications, planning, memory, evaluation, and guardrail patterns.
Lead deep architecture reviews with partner engineering teams, driving tradeoffs across model quality, latency, efficiency, cost, retrieval quality, reliability, safety, security, and observability.
Build hands-on PoCs, benchmarks, reference architectures, and reusable blueprints that help Enterprise ISVs and NVIDIA field teams move from exploration to production.
Guide partners on model customization and post-training workflows, including supervised fine-tuning, reinforcement learning methods, human or AI feedback, direct preference optimization, PEFT/LoRA, synthetic data generation, evaluation, and regression analysis.
Work with agent harnesses and execution environments such as OpenShell, OpenAI Agents SDK, LangGraph, LlamaIndex, LangChain, CrewAI, Semantic Kernel, or similar frameworks.
Translate partner deployment findings into actionable feedback for NVIDIA Product and Engineering, so we can improve our platforms, tools, and field guidance.
What we need to see:
BS/MS/PhD in Computer Science, Electrical Engineering, AI/ML, or equivalent experience.
8+ years of engineering, solutions architecture, applied ML, or technical deployment experience.
Consistent track record leading complex AI, ML, distributed systems, or enterprise software deployments from prototype to production.
Hands-on experience building LLM, generative AI, RAG, or agentic AI applications in production or production-like environments.
Strong programming and debugging skills in Python and Linux environments, with experience in PyTorch, TensorFlow, or similar deep learning frameworks.
Deep understanding of agentic AI architectures, including tool use, orchestration, memory, retrieval, planning, evaluation, guardrails, and failure handling.
Experience with model customization or post-training techniques such as SFT, RL/RLHF/RLAIF, DPO or relevant equivalent experience, reward modeling, LoRA/PEFT, quantization-aware optimization, or model evaluation.
Ability to lead ambiguous partner engagements, influence senior engineering collaborators, and communicate clearly with technical and executive audiences.
Ways to stand out from the crowd:
Hands-on experience with NVIDIA AI software such as NIM, NeMo Framework, NeMo Retriever, NeMo Guardrails, NeMo Agent Toolkit, Dynamo, Nemotron, Triton, TensorRT-LLM, or NIM Operator.
Experience building post-training pipelines for reasoning, tool use, domain adaptation, enterprise task performance, or agent behavior improvement.
Experience with agent harnesses, sandboxed execution, policy enforcement, OpenShell-like environments, or secure enterprise agent runtime build.
Recognized expertise in RAG, model customization, agent orchestration, enterprise AI security, or GPU-accelerated AI infrastructure, with field-facing technical presence through workshops, architecture reviews, talks, whitepapers, or developer enablement.
Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/
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.You will also be eligible for equity and benefits.
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.Skills Required
- BS/MS/PhD in Computer Science, Electrical Engineering, AI/ML, or equivalent experience
- 8+ years of engineering, solutions architecture, applied ML, or technical deployment experience
- Proven track record leading complex AI, ML, distributed systems, or enterprise software deployments from prototype to production
- Hands-on experience building LLM, generative AI, RAG, or agentic AI applications in production or production-like environments
- Strong programming and debugging skills in Python and Linux environments with experience in PyTorch, TensorFlow, or similar deep learning frameworks
- Deep understanding of agentic AI architectures including tool use, orchestration, memory, retrieval, planning, evaluation, guardrails, and failure handling
- Experience with model customization and post-training techniques (SFT, RL/RLHF/RLAIF, DPO, reward modeling, LoRA/PEFT, quantization-aware optimization, evaluation/regression analysis)
- Experience working with agent harnesses and execution frameworks (OpenShell, OpenAI Agents SDK, LangGraph, LlamaIndex, LangChain, CrewAI, Semantic Kernel, or similar)
- Ability to lead ambiguous partner engagements, influence senior engineering collaborators, and communicate clearly with technical and executive audiences
- Hands-on experience with NVIDIA AI software (NIM, NeMo, Triton, TensorRT-LLM, Nemotron, Dynamo, NIM Operator) and GPU-accelerated infra
- Experience building post-training pipelines for reasoning, tool use, domain adaptation, or secure enterprise agent runtimes (sandboxing, policy enforcement)
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
