Global AI Solutions Architect – Model Customization

Amazon Web Services (AWS); East Palo Alto (CA); On-site; USD 177,000 - 239,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Job summary

Amazon Web Services, Inc. is seeking a Machine Learning Solutions Architect (ML SA) to serve as the technical authority on model customization and inference.

You will work with customers across the AMERICAS to design end-to-end ML architectures, from data prep to distributed training, fine-tuning, and production deployment on AWS. You will bridge customer needs with AWS engineering, shaping product roadmaps and delivering cost-efficient, scalable ML solutions at scale.

Qualifications

• 3+ years of experience in technology domains such as software development, cloud computing or infrastructure.
• 3+ years design, implementation, or consulting in applications and infrastructures.
• 5+ years IT development or implementation/consulting in software or Internet industries.

Responsibilities

• Solution design & delivery: architect end-to-end ML solutions leveraging AWS AI/ML services with model customization and inference optimization.
• Technical leadership: act as SME on model customization and inference patterns for SageMaker AI & HyperPod.
• Customer adoption & revenue impact: work with teams to accelerate SageMaker AI adoption across the AMERICAS.
• Thought leadership & evangelism: write blogs, whitepapers, reference architectures and present at events.
• Voice of the customer: capture feature requests and drive platform improvements.
• Community building: scale an internal ML expert community across the AMERICAS.

Skills

Software development

Cloud computing

Systems engineering

Infrastructure

Security

Networking

Data & analytics

Tools

SageMaker AI

SageMaker HyperPod

Bedrock

Job description

Amazon Web Services, Inc. is seeking a Machine Learning Solutions Architect (ML SA) to serve as the technical authority on model customization and inference.

You will work with customers across the AMERICAS to design end-to-end ML architectures, from data prep to distributed training, fine-tuning, and production deployment on AWS. You will bridge customer needs with AWS engineering, shaping product roadmaps and delivering cost-efficient, scalable ML solutions at scale.