> Markdown version of [/jobs/ext/1312331-senior-solutions-architect-agentic-ai](https://www.wearedevelopers.com/jobs/ext/1312331-senior-solutions-architect-agentic-ai). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Solutions Architect, Agentic AI - **Company:** NVIDIA Ltd. - **Location:** Santa Clara, CA, United States - **Experience:** Expert - **Salary:** $184,000.0 - $287,500.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Computer Programming, Software Debugging, Distributed Systems, Python (Programming Language), Linux System Administration, Machine Learning, Regression Analysis, Tensorflow, AI Infrastructure, Reinforcement Learning, Enterprise Software Applications, Pytorch, Large Language Models, Multi-Agent Systems, Deep Learning, Model Validation, Generative AI, Build Management, Information Technology, Low Latency, TensorRT, Virtual Agents, Nim (Programming Language), Data Generation - **Published:** July 17, 2026 - **Apply:** https://www.disabledperson.com/jobs/73717139-senior-solutions-architect-agentic-ai ## About the Role * 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. ## Description * 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. ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Nemotron: NVIDIA's open model strategy for developers](https://www.wearedevelopers.com/videos/100064-nemotron-nvidia-s-open-model-strategy-for-developers) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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