AI/ML Specialist Solutions Architect, Enterprise, AGS US Specialist SA

About the role

AWS Global Sales drives adoption of the AWS cloud worldwide, enabling customers of all sizes to innovate and expand in the cloud. Our team empowers every customer to grow by providing tailored service, unmatched technology, and committed support. We dive deep to understand each customer's unique challenges, then craft innovative solutions that accelerate their success.

As a GenAI/ML Solutions Architect, you will be the Subject Matter Expert for helping customers in the United States design solutions leveraging our GenAI and ML services. You will work as an overlay to field sales teams, covering customers across multiple verticals and helping them architect and adopt solutions using Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker AI, Amazon Nova models, and the broader AI/ML portfolio. You will design RAG architectures, agentic AI workflows, model customization strategies, responsible AI implementations, and production-scale inference pipelines. You will interact with other SAs in the field, providing guidance on their customer engagements, and develop blog posts, reference implementations, workshops, and presentations to enable customers to fully leverage generative AI on AWS. Additionally, you will act as the voice of the customer, working closely with service teams to submit product feature requests and drive the platform forward.

Travel up to 30% across the United States may be possible.

Responsibilities

• Work with customers' development, data science, and AI engineering teams to deeply understand their business and technical needs, then design solutions using AWS AI/ML services such as Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker AI, Amazon Nova foundation models, Amazon Quick, Kiro, Amazon Comprehend, Amazon Rekognition, Amazon Textract, and Amazon Transcribe.
• Partner with SAs, Sales, Business Development, and AI/ML service teams to accelerate customer adoption and revenue attainment for AWS generative AI and machine learning services, with a focus on Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker AI, and the agentic AI portfolio.
• Evangelize AWS GenAI/ML services and share best practices through AWS blogs, whitepapers, reference architectures, sample code repositories, and public-speaking events such as AWS Summit and AWS re:Invent.
• Act as a technical liaison between customers and AWS service teams (Bedrock, AgentCore, SageMaker, and broader AI/ML) to provide customer-driven product improvement feedback and feature requests.
• Develop and support an AWS internal community of GenAI-related subject matter experts in the AMERICAS, enabling field teams to identify, qualify, and position generative AI and agentic AI opportunities with their customers.

A day in the life

Most of your time is spent working directly with customers, helping them figure out how to use generative AI and machine learning to solve real business problems. On a given morning, you might be on a video call with a team of engineers at a large company who want to build an AI agent that can process invoice documents. You're sketching out an architecture on a virtual whiteboard, asking questions about their data, and helping them think through tradeoffs between different approaches. That afternoon, you're prepping a demo for a different customer who's evaluating AWS against a competitor for a conversational AI use case.

Later in the week, you might be on-site running a workshop where a customer's ML team is building their first retrieval-augmented generation pipeline with you guiding them through it hands-on. You typically focus on a single industry (e.g., financial services, healthcare, or manufacturing), building familiarity with the problems, regulations, and data challenges in that space. You'll work with many different companies within your industry rather than being embedded at one or two for years.

Between customer conversations, you build resources to help others learn: writing blog posts about patterns you've seen work well, recording demos, or creating reference architectures for peers across the country. You also help other technical teams understand how to spot AI/ML opportunities in their customer conversations.

Some weeks bring unique opportunities, such as presenting a customer success story at re:Invent, getting early access to a new service and shaping its development, or explaining agentic AI to a Fortune 500 CTO in an executive briefing. Travel runs about 20-30%, mostly for customer workshops, executive briefings, and AWS events.

Requirements

• Deep technical experience working with technologies related to generative AI, machine learning, and/or deep learning.
• Hands-on experience building applications on foundation models, including RAG pipelines, agent frameworks, prompt engineering, model evaluation, and fine-tuning.
• Familiarity with the GenAI ecosystem (model providers, orchestration frameworks, vector databases, evaluation tools).
• Strong mathematics and statistics background is preferred.
• Experience with solution architecture and production ML systems.

Qualifications

Basic Qualifications

• 4+ years of experience in specific technology domain areas (e.g., software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics).
• 2+ years of experience in design, implementation, or consulting in applications and infrastructures.

Preferred Qualifications

• Experience working within software development or Internet-related industries.
• Experience migrating or transforming legacy customer solutions to the cloud.
• Experience working with AWS technologies from a dev/ops perspective.

About the team

Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.

Why AWS

• Inclusive Team Culture: AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams.
• Mentorship & Career Growth: We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. You’ll find endless knowledge-sharing, mentorship, and career-advancing resources to help you develop into a better-rounded professional.
• Work/Life Balance: We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture.

Pay

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance, and optional supplemental life plans), EAP, and mental health support.