Senior Solutions Architect, Generative AI Specialist

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +5

Senior Solutions Architect specializing in Generative AI, focusing on building and architecting enterprise-grade agentic AI systems, RAG pipelines, and multi-modal workflows. The role involves leading prototyping, proof-of-concept collaborations, and providing technical advisory to sophisticated AI partners, with a strong emphasis on GPU-accelerated inference at scale, production optimization, and creating reusable technical assets. Responsibilities include problem-solving across the AI stack, collaborating with internal teams, and contributing to team growth and practice building.

What you'd actually do

  1. Build and architect enterprise-grade agentic AI systems, retrieval-augmented generation (RAG) pipelines, and multi-modal workflows, delivering high-performance, GPU-accelerated inference at scale.
  2. Lead end-to-end prototyping engagements by defining requirements and crafting reference architectures that guide partners from initial concept to production-ready handoffs.
  3. Diagnose and resolve intricate system issues and performance bottlenecks across the full AI stack, from initial model selection to large-scale deployment.
  4. Produce reusable technical assets, including implementation guides and benchmarks, that accelerate time-to-value for both partners and their customers.
  5. Serve as the main technical contact for sophisticated AI collaborators, leading in-depth technical relationships and coordinating their roadmaps with NVIDIA's platform and emerging technology.

Skills

Required

  • 8+ years of relevant engineering experience crafting, developing, and deploying AI/ML systems and sophisticated LLM workflows.
  • MS or advanced degree in Computer Science, AI, or a related field—or equivalent experience.
  • Deep proficiency in advanced GenAI techniques, including retrieval-augmented generation (RAG), prompt engineering, and production inference optimization.
  • Familiarity with agentic AI standards and expertise in building robust, production-grade multi-agent and multimodal architectures.
  • Hands-on experience with model fine-tuning (PEFT, LoRA), synthetic data generation, and building automated evaluation frameworks.
  • Strong command of GPU-optimized infrastructure, containerization, and MLOps pipelines for distributed training and inference.
  • Understanding of AI observability and responsible AI practices, including guardrail implementation and regulatory compliance like GDPR or HIPAA.
  • Experience bridging cloud and on-premises deployments and familiarity with Physical AI concepts such as edge inference.
  • Proven track record of communicating complex technical concepts to both developer teams and executive stakeholders.

Nice to have

  • A tangible body of work demonstrating your builder foundation: open-source contributions, publications, patents, recorded talks, technical blogs, or equivalent public artifacts.
  • Skilled in NVIDIA's AI software suite including NVIDIA AI Enterprise (NVAIE), NIM inference microservices, NeMo, NeMo Curator, Nemotron model family, NVIDIA Blueprints, TensorRT, Triton Inference Server, CUDA libraries, and NVIDIA Agent Intelligence toolkit (NAI/NAT). Knowledge of new NVIDIA frameworks like NemoClaw/OpenShell.
  • Experience engaging with Physical AI platforms like NVIDIA Omniverse, Isaac Sim, Jetson edge computing, or in production robotics/autonomous systems implementations.
  • Active participation in the AI community: conference presentations, open-source project leadership, or recognized expertise in a specific GenAI domain.

What the JD emphasized

  • 8+ years of relevant engineering experience crafting, developing, and deploying AI/ML systems and sophisticated LLM workflows.
  • Deep proficiency in advanced GenAI techniques, including retrieval-augmented generation (RAG), prompt engineering, and production inference optimization.
  • Familiarity with agentic AI standards and expertise in building robust, production-grade multi-agent and multimodal architectures.
  • Hands-on experience with model fine-tuning (PEFT, LoRA), synthetic data generation, and building automated evaluation frameworks.
  • Strong command of GPU-optimized infrastructure, containerization, and MLOps pipelines for distributed training and inference.
  • Understanding of AI observability and responsible AI practices, including guardrail implementation and regulatory compliance like GDPR or HIPAA.

Other signals

  • building enterprise-grade agentic AI systems
  • RAG pipelines
  • multi-modal workflows
  • GPU-accelerated inference at scale
  • production inference optimization
  • multi-agent and multimodal architectures
  • model fine-tuning
  • automated evaluation frameworks
  • distributed training and inference
  • edge inference