Sr. AI/ML Specialist Solutions Architect, AGS-WWSO NAMER Specialist Solutions Architects

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. This customer-first approach is how we built the world's most adopted cloud. Join us and help us grow.

Are you passionate about Generative AI, Agentic AI, and Machine Learning? Are you passionate about helping customers design and build solutions leveraging the most comprehensive GenAI/ML platform available? Come join us!

At Amazon, we've been investing deeply in artificial intelligence for over 25 years, and many of the capabilities customers experience in our products are driven by machine learning. Amazon.com's recommendations engine is driven by ML, as are the paths that optimize robotic picking routes in our fulfillment centers. Our supply chain, forecasting, and capacity planning are informed by ML algorithms.

Alexa is fueled by Natural Language Understanding and Automated Speech Recognition with deep learning. More recently, we've put generative AI at the core of every Amazon business, from coding assistants that help our developers ship faster, to AI agents that automate complex operational workflows, to foundation models that power entirely new customer experiences.

We have thousands of engineers at Amazon committed to pushing the frontier of AI, and it's a big part of our heritage.

Within AWS, we bring that knowledge and capability to customers through three layers of the AI stack: 1) AI Infrastructure with purpose-built chips like AWS Trainium and Inferentia, GPU-powered instances, and optimized frameworks like PyTorch and JAX, 2) AI/ML Platforms including Amazon Bedrock for building generative AI applications with foundation models, agents, guardrails, and knowledge bases, and Amazon SageMaker AI for end-to-end model building, training, and deployment, and 3) AI Application Services like Amazon Quick and Kiro (developer and business productivity), Amazon Nova foundation models, Amazon Transcribe, Amazon Textract, Amazon Comprehend, and Amazon Rekognition for quickly adding intelligence to applications.

AWS is looking for a GenAI/ML Solutions Architect who 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. You will develop blog posts, reference implementations, workshops, and presentations to enable customers to fully leverage generative AI on AWS. Additionally, as the voice of the customer, you will work closely with service teams and submit product feature requests to drive the platform forward.

You must have deep technical experience working with technologies related to generative AI, machine learning, and/or deep learning. Hands-on experience building applications on foundation models (RAG pipelines, agent frameworks, prompt engineering, model evaluation, fine-tuning) is required. Building multi-agent architectures, deploying AI agents hands-on experience is required.

Hands-on experience on agent platforms like Bedrock AgentCore (or equivalent in other public clouds) is strongly preferred. A deep understanding of context engineering, token optimization is needed. A strong mathematics and statistics background is preferred in addition to experience with solution architecture and production ML systems.

You should be familiar with the GenAI ecosystem (model providers, orchestration frameworks, vector databases, evaluation tools) and will leverage this knowledge to help AWS customers evaluate tradeoffs and accelerate their AI adoption.

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

Key job responsibilities

Working with customers' development, data science, and AI engineering teams to deeply understand their business and technical needs. After understanding their needs, you will design solutions that make the best use of the AWS cloud platform and AWS AI/ML services including 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 the AI/ML service teams to accelerate customer adoption and revenue attainment in the AMERICAS 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.

• Thought Leadership: Evangelize AWS GenAI/ML services and share best practices through forums such as AWS blogs, whitepapers, reference architectures, sample code repositories, and public-speaking events such as AWS Summit, AWS re:Invent, etc.
• Act as a technical liaison between customers and the AWS Bedrock, AgentCore, SageMaker, and broader AI/ML service teams 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 insurance company who want to build an AI agent that can process claims 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're 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're typically focused on a single industry (think financial services, or healthcare, or manufacturing), so you build real 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. Some engagements last a few weeks, others stretch over a couple months, but the variety keeps things interesting.

Between customer conversations, you're building things that help others learn what you know: writing a blog post about a pattern you've seen work well, recording a short demo, or building a reference architecture that your peers across the country can reuse. You're also spending time helping other technical teams across the org understand how to spot AI/ML opportunities in their customer conversations.

Beyond the regular rhythm, some weeks bring unexpected moments that make this role special. You might get asked to present a customer success story to an audience of thousands at re:Invent, or get early access to a new service months before launch and help shape how it works based on what you've seen customers struggle with. Occasionally you'll find yourself in an executive briefing room explaining agentic AI to a Fortune 500 CTO who's deciding where to place a multi-million dollar bet.

Travel runs about 20-30%, mostly for customer workshops, executive briefings, and AWS events.

Basic Qualifications

• 8+ years of specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics) experience
• 3+ years of design, implementation, or consulting in applications and infrastructures experience
• 10+ years of IT development or implementation/consulting in the software or Internet industries experience

Preferred Qualifications

• 5+ years of infrastructure architecture, database architecture and networking experience
• Experience working with end user or developer communities
• Knowledge of cloud architecture
• Experience developing, deploying and managing AI products at scale
• Experience in coding agents

Additional information

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.

Pay and benefits

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 option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave.

USA, CA, San Francisco - 176,600.00 - 239,000.00 USD annually