Senior Solutions Architect, Retail

NVIDIA NVIDIA · Semiconductors · CA · Remote

Senior Solutions Architect for Retail at NVIDIA, focusing on developing and deploying Agentic AI solutions for enterprise clients. The role involves building complex agentic systems, RAG pipelines, and optimizing inference performance using NVIDIA's AI infrastructure. Requires strong programming skills, experience with LLM applications, and agentic frameworks.

What you'd actually do

  1. Build complex agentic systems featuring multi-agent coordination, long-horizon reasoning, and advanced planning frameworks.
  2. Develop full-scale solutions, including domain-specific enterprise agents and high-performance retrieval pipelines (RAG) spanning various data sources.
  3. Optimize inference performance by bringing to bear GPU-accelerated frameworks and the full NVIDIA AI infrastructure stack.
  4. Build hands-on PoCs and reference architectures that serve as the blueprint for production-grade generative AI pipelines.
  5. Collaborate alongside Enterprise ISVs to integrate NVIDIA software into native platforms, accelerating the deployment of production workloads.

Skills

Required

  • BS/MS/PhD in Computer Science, Electrical Engineering, AI/ML, or equivalent experience.
  • 8+ years of experience in deep learning, machine learning, or distributed AI systems.
  • Strong programming and debugging experience in Python, C/C++, and Linux environments.
  • Background in using deep learning libraries like PyTorch or TensorFlow.
  • Hands-on experience building LLM and generative AI applications.
  • Experience working with agentic or multi-agent AI systems employing frameworks such as: LangGraph, LlamaIndex, CrewAI, LangChain, OpenAI Agents SDK or similar orchestration frameworks
  • Experience building tool-using AI agents that interact with APIs, databases, and enterprise systems.
  • Ability to rapidly prototype AI applications and build scalable GPU-accelerated architectures.

Nice to have

  • Experience working with NVIDIA GPUs and AI software, such as NVIDIA NIM, NeMo Framework, NeMo Retriever, and NeMo Agent Toolkit.
  • Background with LLM evaluation frameworks, benchmarking systems, and safety guardrails for agentic workflows.
  • Experience with pre-training/fine-tuning techniques like SFT, LoRA, DPO, PPO, GRPO, DAPO, or RLVF
  • Experience optimizing reasoning-focused LLMs through timely engineering, quantization, or benchmarking.
  • Background with parallel or distributed computing environments and AI workloads optimized for GPUs.

What the JD emphasized

  • 8+ years of experience in deep learning, machine learning, or distributed AI systems.
  • Hands-on experience building LLM and generative AI applications.
  • Experience working with agentic or multi-agent AI systems employing frameworks such as: LangGraph, LlamaIndex, CrewAI, LangChain, OpenAI Agents SDK or similar orchestration frameworks
  • Experience building tool-using AI agents that interact with APIs, databases, and enterprise systems.

Other signals

  • AI-native systems
  • agentic AI
  • multi-agent coordination
  • RAG-integrated workflows
  • accelerated inference
  • production-grade deployment
  • enterprise agents