Solutions Architect, AI and ML

NVIDIA NVIDIA · Semiconductors · Redmond, WA +2

Solutions Architect role focused on helping customers adopt NVIDIA's GPU hardware and software for building and deploying AI/ML and data analytics solutions on cloud platforms. Involves customer engagement, technical content delivery, and PoC development.

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

  • 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
  • deep learning frameworks TensorFlow or PyTorch
  • GPU
  • CUDA experience
  • deploying solutions in cloud computing environments including AWS, GCP, or Azure
  • Knowledge of DevOps/ML Ops technologies such as Docker/containers, Kubernetes, data center deployments
  • Ability to use at least one scripting language (i.e., Python)
  • Good programming and debugging skills
  • Ability to communicate your ideas/code clearly through documents, presentation etc.

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

  • GPU hardware and Software
  • Machine Learning (ML) , Deep Learning (DL)
  • Cloud Computing Platforms
  • developers, researchers, and data scientists
  • end-to-end technology solutions
  • AI/ML solutions at scale
  • customer’s critical business needs
  • ML/DL and other software solutions
  • Deep Learning and Machine Learning
  • deep learning frameworks TensorFlow or PyTorch, GPU, and CUDA experience extremely helpful
  • deploying solutions in cloud computing environments
  • DevOps/ML Ops technologies
  • NVIDIA GPUs and SDKs
  • Deep Learning at scale

Other signals

  • Deploying AI/ML solutions at scale
  • Customer-facing technical expertise
  • Driving end-to-end technology solutions