Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply cutting edge Generative AI algorithms to solve real world problems with significant impact?Artificial Intelligence (AI) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.In this role, you are a passionate, talented, and inventive AI specialist with a strong background in ML and AI, pushing the boundaries in Generative AI, Time Series, Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Computer Vision, Customer Segmentation, Fraud Prevention, Capacity Planning, Predictive Maintenance, and more. We’re looking for talented scientists who can leverage ML, Generative AI, and other techniques to design, implement, fine-tune, and evangelize state-of-the-art solutions for never-before-solved problems.Key job responsibilities- Design and develop robust and scalable AI solutions for business problems- Interact with customer directly to understand the business problem, help and aid them in implementation of their ML ecosystem- Effectively use Foundation Models available on Amazon Bedrock and Amazon SageMaker to meet our customer’s performance needs- Analyze and extract relevant information from large amounts of historical data to help automate and optimize key processes- Strong communication skills, with attention to detail and ability to convey rigorous mathematical concepts and considerations to non-experts- Comfortable working in a fast paced, highly collaborative, dynamic work environment- Mentor and develop members on the teamAbout the teamThe AI Delivery Team at Professional Services helps AWS customers accelerate the use of AI to solve business and operational challenges and promote innovation in their organization.The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select, train, and fine-tune the right models, define strategies to overcome technical or business challenges, build proof-of-concepts, and manage the scalable machine learning lifecycle to deliver robust, production-ready solutions.