Senior Solutions Architect, Agentic AI
$184,000–$287,500 year
RemoteUnited States or Santa Clara, California, United States
Job Summary
Lead technical delivery for strategic Agentic AI partner engagements from discovery through production rollout. Design and build enterprise-grade agentic systems, including multi-agent workflows, RAG-integrated applications, and tool-using agents. Conduct deep architecture reviews with partner engineering teams to drive tradeoffs across model quality, latency, and security. Build hands-on PoCs, benchmarks, and reusable blueprints to accelerate partner adoption of NVIDIA technologies. Guide partners on model customization and post-training workflows, including supervised fine-tuning and reinforcement learning methods. Translate deployment findings into actionable feedback for NVIDIA Product and Engineering to improve platforms and field guidance.
Required Qualifications
- 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
- 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
Desired Qualifications
- 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
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