Sr. Solutions Architect, AI/ML, Product Acceleration SA
Amazon.com
Are you passionate about Artificial Intelligence, Machine Learning, and Generative AI? Are you passionate about helping software customers build solutions leveraging the state-of-the-art AI/ML/GenAI tools on Amazon Web Service (AWS)? Come join us!
At Amazon, we've been investing deeply in artificial intelligence for over 20 years, and many of the capabilities customers experience in our products are driven by machine learning. From Amazon.com's recommendations to Alexa's natural language processing and our supply chain optimization, machine learning is integrated throughout our operations and services. We have thousands of engineers at Amazon committed to machine learning and deep learning, and it's a big part of our heritage.
Within AWS, we’re focused on bringing that knowledge and capability to customers through three layers of the AI stack: 1) Frameworks and Infrastructure with tools like Apache MxNet and TensorFlow, 2) Machine Learning Platforms such as Amazon SageMaker for data scientists, and, 3) API-driven Services like Amazon Bedrock, Amazon Lex, Amazon Kendra, Amazon Transcribe, Amazon Comprehend, and Amazon Rekognition to quickly add intelligence to applications with simple API calls.
AWS is looking for a Solutions Architect (ML SA), who will be the Subject Matter Expert (SME) for helping customers in the design of solutions that leverage our AI/ML/GenAI services, including Amazon Bedrock, Amazon SageMaker, and Amazon Q. As part of the team, you will work closely with customers to enable large-scale use cases, design GenAI pipelines, and drive the adoption of AWS for the AI/ML platforms. You will work closely with AWS product development experts and AI/ML advisors, and interact with other SAs in the field, providing guidance on their customer engagements, and you will develop presentations, demos, workshops, blogs, and reference implementations to enable customers to fully leverage AI/ML/GenAI on AWS. Additionally, as the voice of the customer, you will work closely with the AWS service teams, and submit product feature requests to drive the platform forward.
You must have deep technical experience working with technologies related to artificial intelligence, machine learning and/or deep learning. A strong mathematics and statistics background is preferred in addition to experience building complex machine learning models. You will be familiar with the ecosystem of software vendors in the GenAI space, and will leverage this knowledge to help AWS customers in their selection process.
Travel Requirements: This role may require up to 30% travel.
About the team
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the 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?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.
Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here 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. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
At Amazon, we've been investing deeply in artificial intelligence for over 20 years, and many of the capabilities customers experience in our products are driven by machine learning. From Amazon.com's recommendations to Alexa's natural language processing and our supply chain optimization, machine learning is integrated throughout our operations and services. We have thousands of engineers at Amazon committed to machine learning and deep learning, and it's a big part of our heritage.
Within AWS, we’re focused on bringing that knowledge and capability to customers through three layers of the AI stack: 1) Frameworks and Infrastructure with tools like Apache MxNet and TensorFlow, 2) Machine Learning Platforms such as Amazon SageMaker for data scientists, and, 3) API-driven Services like Amazon Bedrock, Amazon Lex, Amazon Kendra, Amazon Transcribe, Amazon Comprehend, and Amazon Rekognition to quickly add intelligence to applications with simple API calls.
AWS is looking for a Solutions Architect (ML SA), who will be the Subject Matter Expert (SME) for helping customers in the design of solutions that leverage our AI/ML/GenAI services, including Amazon Bedrock, Amazon SageMaker, and Amazon Q. As part of the team, you will work closely with customers to enable large-scale use cases, design GenAI pipelines, and drive the adoption of AWS for the AI/ML platforms. You will work closely with AWS product development experts and AI/ML advisors, and interact with other SAs in the field, providing guidance on their customer engagements, and you will develop presentations, demos, workshops, blogs, and reference implementations to enable customers to fully leverage AI/ML/GenAI on AWS. Additionally, as the voice of the customer, you will work closely with the AWS service teams, and submit product feature requests to drive the platform forward.
You must have deep technical experience working with technologies related to artificial intelligence, machine learning and/or deep learning. A strong mathematics and statistics background is preferred in addition to experience building complex machine learning models. You will be familiar with the ecosystem of software vendors in the GenAI space, and will leverage this knowledge to help AWS customers in their selection process.
Travel Requirements: This role may require up to 30% travel.
About the team
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the 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?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.
Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here 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. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
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