Description

Are you passionate about Artificial Intelligence, Machine Learning, and Generative AI? Are you passionate about helping 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. Amazon.com’s recommendations engine is driven by machine learning (ML), as are the paths that optimize robotic picking routes in our fulfillment centers. Our supply chain, forecasting, and capacity planning are also informed by ML algorithms. Alexa is fueled by Natural Language Understanding and Automated Speech Recognition deep learning; as is Prime Air, and the computer vision technology in our new retail experience, Amazon Go. 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 Machine Learning Solutions Architect (ML SA), who will be the Subject Matter Expert (SME) for helping SLG-EDU customers in the U.S. design solutions that leverage our 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 interact with other SAs in the field, providing guidance on their customer engagements, and you will develop white papers, blogs, reference implementations, and presentations to enable customers to fully leverage GenAI on AWS. Additionally, as the voice of the customer, you will work closely with the 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 up to 30% across the U.S. may be possible.

Key job responsibilities

The WWPS SLG-EDU AI/ML Specialist SA team builds technical relationships with customers of all sizes and operate as their trusted advisor, ensuring they get the most out of the cloud at every stage of their journey while adopting Machine Learning across their organization.

You’ll manage the overall technical relationship between AWS and our customers, making recommendations on security, cost, performance, reliability and operational efficiency to accelerate their challenging Machine Learning projects.

Internally, you will be the voice of the customer, sharing their needs with regard to their usage of our services impacting the roadmap of AWS AI/ML features.

In this role, your creativity will link technology to tangible solutions, with the opportunity to define cloud-native Machine Learning architectural patterns for a variety of use cases.

You will participate in the creation and sharing of best practices, technical content and new reference architectures (e.g. white papers, code samples, blog posts) and evangelize and educate about running Machine Learning workloads on AWS technology (e.g. through workshops, user groups, meetups, public speaking, online videos or conferences).

If you can educate AWS customers about the art of the possible, while challenging the impossible, come build the future with us.

We are open to hiring candidates to work out of one of the following locations:

Austin, TX, USA | Boston, MA, USA | Chicago, IL, USA | New York City, NY, USA | Seattle, WA, USA | Washington Dc, DC, USA

Basic Qualifications

• 3+ years of design, implementation, or consulting in applications and infrastructures experience

• 5+ years of specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics) experience

• Experience within AI/ML / GenAI technical domain (e.g. multimodal ML, model evaluation, MLOps, MLSecOps, ML training, Inference, Data Engineering, Data Science, Fine-tuning to prompt engineering, Responsible AI)

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

• Knowledge of software development tools and methodologies

• Experience in IT development or implementation/consulting in the software or Internet industries

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $105,100/year in our lowest geographic market up to $204,300/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.