GenAI/ML Solutions Architect — AI Innovation Lead

Amazon Web Services (AWS); Atlanta (GA); On-site; USD 131,000 - 178,000; Full time

Job summary

Amazon Web Services, Inc. seeks a Machine Learning Solutions Architect (ML SA) to be the SME for helping customers in the AMERICAS design ML solutions leveraging AWS ML services.

You will work with ISVs to enable large-scale use cases, design ML pipelines, and drive AWS AI/ML adoption. You will collaborate with field SAs, service teams, and customers to deliver guidance, develop white papers, and create reference implementations that demonstrate best practices in GenAI and ML on AWS.

Qualifications

• 2+ years design, implementation, or consulting in applications and infrastructures experience.
• Experience building ML solutions for production environments.
• Ability to partner with cross-functional teams to drive ML adoption.

Responsibilities

• Design AWS-based ML solutions utilizing SageMaker, Bedrock, and related AI/ML services.
• Partner with SAs, Sales, Business Development and AI/ML Service teams to accelerate customer adoption and revenue for GenAI use cases.
• Publish thought leadership through blogs, whitepapers, and reference architectures; present at events such as AWS Summit or re:Invent.
• Act as a technical liaison between customers and AWS SageMaker or other service teams to provide customer-driven product feedback.
• Develop and support an internal GenAI community in the AMERICAS.

Skills

ML design

Cloud computing

GenAI/ML

AWS SageMaker

AI/ML customer engagement

Tools

SageMaker

Bedrock

PyTorch

TensorFlow

Job description

Amazon Web Services, Inc. seeks a Machine Learning Solutions Architect (ML SA) to be the SME for helping customers in the AMERICAS design ML solutions leveraging AWS ML services.

You will work with ISVs to enable large-scale use cases, design ML pipelines, and drive AWS AI/ML adoption. You will collaborate with field SAs, service teams, and customers to deliver guidance, develop white papers, and create reference implementations that demonstrate best practices in GenAI and ML on AWS.