Solutions Architect, AI and ML

NVIDIA NVIDIA · Semiconductors · Redmond, WA +2

This role focuses on assisting customers in adopting NVIDIA's GPU hardware and software for building and deploying AI/ML and data analytics solutions on cloud platforms. The Solutions Architect acts as a technical expert, engaging with developers, researchers, and data scientists, and partnering with sales teams to drive end-to-end technology solutions.

What you'd actually do

  1. Working with Cloud Service Providers to develop and demonstrate solutions based on NVIDIA’s ML/DL and data science software and hardware technologies
  2. Build and deploy AI/ML solutions at scale using NVIDIA's AI software on cloud-based GPU platforms.
  3. Build custom PoCs for solution that address customer’s critical business needs applying NVIDIA hardware and software technology
  4. Partner with Sales Account Managers or Developer Relations Managers to identify and secure new business opportunities for NVIDIA products and solutions for ML/DL and other software solutions
  5. Prepare and deliver technical content to customers including presentations about purpose-built solutions, workshops about NVIDIA products and solutions, etc.

Skills

Required

  • Solutions Engineering or Sales Engineering experience
  • Deep Learning and Machine Learning experience
  • Deep learning frameworks TensorFlow or PyTorch
  • GPU and CUDA experience
  • Deploying solutions in cloud computing environments (AWS, GCP, Azure)
  • DevOps/ML Ops technologies (Docker/containers, Kubernetes, data center deployments)
  • Scripting language (Python)
  • Programming and debugging skills
  • Communication skills (documents, presentation)

Nice to have

  • AWS, GCP or Azure Professional Solution Architect Certification
  • Hands-on experience with NVIDIA GPUs and SDKs (e.g. CUDA, RAPIDS, Triton etc.)
  • System-level experience specifically GPU-based systems
  • Experience with Deep Learning at scale
  • Familiarity with parallel programming and distributed computing platforms

What the JD emphasized

  • 3+ years of Solutions Engineering (or similar Sales Engineering roles) or equivalent experience
  • 3+ years of work-related experience in Deep Learning and Machine Learning
  • Established track record of deploying solutions in cloud computing environments including AWS, GCP, or Azure

Other signals

  • Deploying ML/DL solutions at scale
  • Customer-facing technical expertise
  • GPU hardware and software adoption