GenAI & ML Solutions Architect — Scale & Inference

Amazon Web Services (AWS); New York (NY); On-site; USD 169,000 - 229,000; Full time

Job summary

Amazon Web Services invites a Machine Learning Solutions Architect (ML SA) to serve as the technical authority on model customization and inference, helping customers across the AMERICAS unlock foundation models, custom training, and production-scale serving on AWS. You will design end-to-end ML architectures spanning data prep, distributed training, fine-tuning, and efficient inference.

This role blends solution design, technical leadership, customer adoption, thought leadership, and community

Qualifications

• 3+ years of technology domain areas experience
• 3+ years of design, implementation, or consulting in applications and infrastructures experience
• 5+ years of IT development or implementation/consulting in the software or Internet industries experience

Responsibilities

• Solution Design & Delivery: Partner with customers' data science and engineering teams to architect AWS AI/ML solutions with emphasis on model customization and inference optimization.
• Technical Leadership: Serve as the go-to SME on model customization and inference patterns across SageMaker AI and SageMaker HyperPod.
• Customer Adoption & Revenue Impact: Accelerate adoption of SageMaker AI across the AMERICAS and contribute to pipeline generation and revenue attainment.
• Thought Leadership & Evangelism: Author technical blogs, whitepapers, reference architectures, and reusable solution artifacts; present at flagship events to establish AWS leadership.
• Voice of the Customer: Act as the technical liaison between customers and AWS SageMaker service teams to drive platform improvements.
• Community Building: Develop and scale an internal ML community across the AMERICAS.

Skills

ML Architecture

Cloud Expertise

SageMaker

Job description

Amazon Web Services invites a Machine Learning Solutions Architect (ML SA) to serve as the technical authority on model customization and inference, helping customers across the AMERICAS unlock foundation models, custom training, and production-scale serving on AWS. You will design end-to-end ML architectures spanning data prep, distributed training, fine-tuning, and efficient inference.

This role blends solution design, technical leadership, customer adoption, thought leadership, and community