Senior ML Solutions Architect - Generative AI & SageMaker

Amazon; Chicago (IL); On-site; USD 154,000 - 208,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Amazon Web Services, Inc. is seeking a Worldwide Specialist Solutions Architect - AI, Data & AI GTM 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, inference optimization, and production deployment, across SageMaker AI and HyperPod.

Qualifications

• 3+ years experience in software/cloud/infra domains.
• 3+ years design, implementation, or consulting in applications and infrastructures.
• 5+ years IT development or implementation/consulting in the software or Internet industries.

Responsibilities

• Design and deliver AI/ML solutions for customers focusing on model customization and inference optimization.
• Provide technical leadership on SageMaker AI and HyperPod, guiding field SAs and customers.
• Accelerate adoption across the AMERICAS to drive pipeline and revenue attainment.
• Author technical blogs, whitepapers, reference architectures, and reusable solution artifacts.
• Capture customer signals to inform product roadmap and feature requests.
• Build and scale a community of ML experts across the AMERICAS.

Skills

3+ years tech domain experience

3+ years design/implementation/consult

5+ years IT development/implementation

Job description

Amazon Web Services, Inc. is seeking a Worldwide Specialist Solutions Architect - AI, Data & AI GTM 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, inference optimization, and production deployment, across SageMaker AI and HyperPod.