Solutions Architect, AI and ML

Jobtailor

California (MO)

On-site

USD 130,000 - 180,000

Full time

14 days+

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Job summary

NVIDIA is seeking a seasoned Solutions Engineer to design and demonstrate AI/ML solutions using NVIDIA technologies on cloud platforms. You will create proofs of concept, collaborate with sales, and deliver technical content to customers.

Ideal candidates have deep learning expertise, cloud deployment experience, and certifications in cloud architecture. This role emphasizes collaboration with customers and cross-functional teams to drive strategic solutions.

Qualifications

BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Statistics, Physics, or a related engineering field, or equivalent experience.
3+ years in Solutions Engineering or similar Sales Engineering, or equivalent experience.
3+ years in Deep Learning and Machine Learning.
Experience with TensorFlow or PyTorch, GPU, and CUDA.
Track record deploying solutions in cloud environments (AWS, GCP, Azure).
Knowledge of DevOps/ML Ops technologies such as Docker/containers, Kubernetes, and data center deployments.
Proficiency in at least one scripting language (Python).
Strong programming and debugging skills; ability to communicate ideas clearly in documents/presentations.
AWS/GCP/Azure Professional Solution Architect certifications are a differentiator.
Hands-on with NVIDIA GPUs and SDKs (CUDA, RAPIDS, Triton) as a differentiator.
System-level experience with GPU-based systems and distributed computing platforms as differentiators.

Responsibilities

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 to identify and secure new opportunities for NVIDIA products and solutions.
Prepare and deliver technical content to customers, including presentations and workshops.
Conduct regular technical customer meetings covering project/product roadmaps and new technologies.
Establish close technical ties with customers to facilitate rapid resolution of issues.
Engage with developers, researchers, and data scientists at strategic technology customers.
Work with business and engineering teams on product strategy and drive end-to-end technology solutions.

Skills

Deep Learning

Machine Learning

TensorFlow

PyTorch

CUDA

Python

Programming & Debugging

Cloud Computing

DevOps/ML Ops

Parallel Programming

Communication Skills

Technical Presentation Skills

Education

BS/MS/PhD in Electrical/Computer Engineering, CS, Statistics, Physics or related field

Tools

Docker/Containers

Kubernetes

NVIDIA GPUs & SDKs

Data Center Deployments

Job description

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.

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