Worldwide Specialist Solutions Architect - GenAI, Amazon Bedrock GTM

About the position

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 Solutions Architect 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 Solution 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.

Responsibilities

• Implement and deploy state of the art machine learning solutions under Gen AI.
• Build prototypes, PoCs, and explore new solutions.
• Evangelize AWS GenAI services and share best practices through forums such as AWS blogs, white-papers, reference architectures and public-speaking events.
• Partner with Data Scientists, SAs, Sales, Business Development and the Generative AI Service teams to accelerate customer adoption.
• Act as a technical liaison between customers and the AWS Generative AI services teams.
• Develop and support an AWS internal community of GenAI related subject matter experts worldwide.
• Create field enablement materials for the broader technical population.

Requirements

• 2+ years of design, implementation, or consulting in applications and infrastructures experience.
• 3+ years of specific technology domain areas experience.
• 1+ year experience working with technologies related to large language models including LLM architectures.
• Proficient with design, deployment, and evaluation of LLM-powered agents and tools.
• 3+ years of experience in design/implementation/consulting for Machine Learning/AI/Deep Learning solutions.
• 5+ years professional experience in software development in languages related to ML like Python or Java.
• Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, engineering, or computer science.

Nice-to-haves

• Experience with optimizing ML workloads using Model compression, distillation, pruning, sparsification, quantization.
• Experience with distributed training and optimizing performance versus costs.
• Experience with open source frameworks for building applications powered by language models like LangChain, LlamaIndex.
• Customer facing skills to represent AWS well within the customer's environment.
• Experience with AWS technologies like SageMaker, Step Functions, OpenSearch, PgVector, S3, IAM, Cognito, EC2, Glue, & EMR.

Benefits

• Flexible working culture.
• Ongoing events and learning experiences.
• Employee-led affinity groups fostering a culture of inclusion.