• 7+ years of experience in end-to-end technical architecture, design, deployment and operations for AI/ML platforms and applications.- 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, function calling, agentic workflows, embedding model fine tuning and retrieval method evaluation and optimization approaches.- 7+ years of experience in design/implementation/consulting for Machine Learning/AI/Deep Learning solutions - using one or more Deep Learning frameworks such as TensorFlow and PyTorch.- 7+ years professional experience in software development in languages related to ML like Python or Java. Experience working with RESTful API and general service-oriented architecture.- 10+ years of specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics) experience.

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.You must have deep technical experience working with technologies related to large language models including LLM architectures, model evaluation, 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. You should understand the security and compliance requirements for ML/GenAI implementations. You must have experience with LangChain, LLAMAIndex, Data Augmentation, Responsible AI, and Performance Evaluation frameworks. You should have experience architecting end to end ML/Gen AI applications for customers using AWS services and Well Architected Framework.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. 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 solutions under Gen AI. You will build prototypes, PoCs, and explore new solutions. You will interact closely with our customers.- 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 GenAI 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.About the teamAmazon 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.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.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.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.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.

• Experience with optimizing ML workloads using Model compression, distillation, pruning, sparsification, quantization, Transformers based algorithms like FlashAttention, PagedAttention, Speculative decoding, Distributed training/inference optimization, Hardware-informed efficient model architecture.
• 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
• Should be able to interact with Chief Data Science Officers, as well as the people within their organizations.
• Experience with AWS technologies like SageMaker, Step Functions, OpenSearch, PgVector, S3, IAM, Cognito, EC2, Glue, & EMR.
• 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.
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $164,500/year in our lowest geographic market up to $284,300/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

• Experience with optimizing ML workloads using Model compression, distillation, pruning, sparsification, quantization, Transformers based algorithms like FlashAttention, PagedAttention, Speculative decoding, Distributed training/inference optimization, Hardware-informed efficient model architecture.
• 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
• Should be able to interact with Chief Data Science Officers, as well as the people within their organizations.
• Experience with AWS technologies like SageMaker, Step Functions, OpenSearch, PgVector, S3, IAM, Cognito, EC2, Glue, & EMR.
• 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