- Work with Cloud Service Providers to develop and demonstrate solutions based on NVIDIA’s ML/DL and data science software and hardware technologies
- Build and deploy AI/ML solutions at scale using NVIDIA's AI software on cloud-based GPU platforms
- Build custom proofs of concept addressing customers’ critical business needs with NVIDIA hardware and software
- Partner with Sales Account Managers or Developer Relations Managers to identify and secure new business opportunities for NVIDIA products and solutions
- Prepare and deliver technical content to customers, including presentations and workshops
- Conduct regular technical customer meetings covering project and product roadmaps, feature discussions, and new technologies
- Establish close technical ties with customers to facilitate rapid resolution of customer issues
- Engage directly with developers, researchers, and data scientists at strategic technology customers
- Work with business and engineering teams on product strategy
- Drive end-to-end technology solutions based on customer business needs
Requirements
- 3+ years of Solutions Engineering or similar Sales Engineering experience, or equivalent experience
- 3+ years of work-related experience in Deep Learning and Machine Learning
- Experience with TensorFlow or PyTorch, GPU, and CUDA
- BS, MS, or PhD in Electrical/Computer Engineering, Computer Science, Statistics, Physics, or another Engineering field, or equivalent experience
- Track record of deploying solutions in cloud computing environments, including AWS, GCP, or Azure
- Knowledge of DevOps/ML Ops technologies such as Docker/containers, Kubernetes, and data center deployments
- Ability to use at least one scripting language, such as Python
- Good programming and debugging skills
- Ability to communicate ideas and code clearly through documents and presentations
- AWS, GCP, or Azure Professional Solution Architect Certification is a differentiator
- Hands-on experience with NVIDIA GPUs and SDKs such as CUDA, RAPIDS, or Triton is a differentiator
- System-level experience with GPU-based systems is a differentiator
- Experience with Deep Learning at scale is a differentiator
- Familiarity with parallel programming and distributed computing platforms is a differentiator
Core Competencies
Demonstrates expertise in building and deploying AI/ML solutions using NVIDIA technologies on cloud platforms, with a strong foundation in Deep Learning and Machine Learning. Capable of engaging with technical stakeholders to drive business solutions and deliver impactful presentations.
Highest-signal resume keywords
- Solutions Engineering Experience
- Deep Learning and Machine Learning
- TensorFlow or PyTorch
- Cloud Computing Environments
- NVIDIA GPUs and SDKs
ATS Optimization Keywords
Hard Skills
- Deep Learning
- Machine Learning
- TensorFlow
- PyTorch
- CUDA
- Scripting Language (Python)
- Programming and Debugging Skills
- Cloud Computing (AWS, GCP, Azure)
- DevOps/ML Ops Technologies
- Parallel Programming
Soft Skills
- Communication Skills
- Technical Presentation Skills
Certifications & Qualifications
- AWS Professional Solution Architect Certification
- GCP Professional Solution Architect Certification
- Azure Professional Solution Architect Certification
Industry Keywords
- AI Solutions
- ML Solutions
- Cloud Service Providers
- Technical Customer Engagement
- Business Strategy
Tools & Technologies
- NVIDIA Hardware and Software
- Docker/Containers
- Kubernetes
- Data Center Deployments