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Amazon Senior Generative AI Data Scientist, Amazon Bedrock Service GTM - Big Bets in Arlington, Virginia

Description

Are you passionate about Generative AI (GenAI)? Do you want to help define the future of Go to Market (GTM) at AWS using generative AI? In this role, you will help some of our largest customers build and deploy GenAI enabled applications using Amazon Bedrock and SageMaker, fine tune and build Generative AI models, and help enterprise customers leverage these models to power end applications. You will engage with AWS product owners to influence product direction and help our customers tap into new markets by utilizing GenAI along with AWS Services.

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.

AWS is looking for a Generative AI Data Scientist, who will be the Subject Matter Expert (SME) for helping customers in designing solutions that leverage our Generative AI services. You will interact with customers directly to understand the business problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths for generative AI. As part of the Generative AI Worldwide Specialist organization, you will work closely with other Data Scientists and Machine Learning Architects from various geographies to enable large-scale customer use cases and drive the adoption of Amazon Web Services for GenAI services. You will interact with other Data Scientists and Solution Architects in the field, providing guidance on their customer engagements. You will develop white papers, blogs, reference implementations, and presentations to enable customers and partners to fully leverage Generative AI services on Amazon Web Services. You will also create field enablement materials for the broader technical field population, to help them understand how to integrate AWS Generative AI solutions into customer architectures. You drive effective feedback gathering from customers, and you distill and translate that feedback into clear business and technical requirements for product and engineering teams to review.

You must have deep technical experience working with technologies related to large language models including LLM architectures, model evaluation, adapters, pre-training and fine-tuning techniques. You should be proficient with design, deployment, and evaluation of LLM-powered agents and tools and orchestration approaches. You must have experience with embedding model fine tuning and retrieval method evaluation approaches.

Candidates must have great communication skills and be very technical, with the ability to impress Amazon Web Services customers at any level, from executive to developer. Previous experience with Amazon Web Services is desired but not required, provided you have experience building large scale solutions. You will get the opportunity to work directly with senior ML engineers and Data Scientists at customers, partners and Amazon Web Services service teams, influencing their roadmaps and driving innovation.

Key job responsibilities

  • Customer Advisor- Implement, and deploy state of the art machine learning algorithms under Gen AI. You will build prototypes, PoCs, and explore new solutions. You will interact closely with our customers and with the academic community.

  • Thought Leadership – Evangelize AWS GenAI services and share best practices through forums such as AWS blogs, white-papers, reference architectures and public-speaking events such as AWS Summit, AWS re:Invent, etc.

  • Partner with Data Scientists, SAs, Sales, Business Development and the Generative AI Service teams to accelerate customer adoption and providing guidance on their customer engagements.

  • Act as a technical liaison between customers and the AWS Generative AI services teams to provide customer driven product improvement feedback.

  • Develop and support an AWS internal community of ML related subject matter experts worldwide. Create field enablement materials for the broader technical population, to help them understand how to integrate AWS GenAI solutions into customer architectures.

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

Arlington, VA, USA | New York, NY, USA | San Francisco, CA, USA | Seattle, WA, USA | Washington Dc, DC, USA

Basic Qualifications

  • 5+ years of Data Scientist or Machine Learning Solutions Architect experience.

  • 5+ years of experience with Python to analyze datasets, train , evaluate, deploy, and optimize models.

  • 3+ Experience with ML frameworks such as PyTorch, TensorFlow, or similar

  • 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience

  • 1+ year experience working with technologies related to large language models including LLM architectures, model evaluation, adapters, model customization including pre-training and fine-tuning techniques.

  • Proficient with design, deployment, and evaluation of LLM-powered agents and tools and orchestration approaches.

  • Proficient with prompt engineering, embedding model fine tuning and retrieval method evaluation and optimization approaches.

  • Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science

Preferred Qualifications

  • Experience as a leader and mentor on a data science team

  • Experience with open source frameworks for building applications powered by language models like LangChain, LlamaIndex.

  • Design, develop, and optimize high-quality prompts and templates that guide the behavior and responses of LLM.

  • Experience with design, deployment, and evaluation of LLM-powered agents and tools and orchestration approaches

  • Customer facing skills to represent AWS well within the customer’s environment and drive discussions with senior personnel regarding trade-offs, best practices, and risk mitigation. Should be able to interact with Chief Data Science Officers, as well as the people within their organizations.

  • Demonstrated ability to think strategically about business, product, and technical challenges in an enterprise environment. Track record of thought leadership and innovation around Machine Learning.

  • Led a cloud initiative as an AWS customer or consulting with a customer in their own IT transformation.

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.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $127,300/year in our lowest geographic market up to $247,600/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. Applicants should apply via our internal or external career site.

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