Join Amazon Customer Service's Associate Experience team as a Software Engineer II. This role is pivotal in shaping the future of how our Customer Service Associates (CSAs) onboard, learn, and improve performance, especially as AI increasingly handles routine interactions. You will be instrumental in reimagining the development and support of one of the world's largest workforces.

This is an exciting opportunity to define and build the Associate Context & Insights platform. This platform will create a dynamic representation of associates using signals from their entire lifecycle, making this context reusable across various customer service products. You will collaborate with senior engineers to architect this new platform, owning its journey from design through implementation, testing, and deployment. This position offers a unique chance to establish an entirely new product space within Amazon.

Define the long-term architecture for the Associate Context & Insights platform. Guide the team in constructing scalable Machine Learning Models. Establish robust engineering excellence standards for the team. Deliver the minimum viable product through incremental development. Influence senior leadership regarding long-term investments that enhance associate capabilities, customer outcomes, and operational efficiency. Partner closely with Engineering, Applied Science, and Business Intelligence teams to continuously refine the product.

A minimum of 3 years of professional software development experience (excluding internships) is required. Possess at least 2 years of experience in designing or architecting new and existing systems, including familiarity with design patterns, reliability, and scaling. Proficiency in at least one modern programming language such as Java, C++, or C#, coupled with a strong understanding of object-oriented design principles. Demonstrated experience in building complex software systems that have been successfully delivered to customers. Solid understanding of Machine Learning and Large Language Model fundamentals, encompassing architecture, training/inference lifecycles, and optimization of model execution. Familiarity with core Machine Learning and LLM concepts, including transformer architecture, training/inference lifecycles, and optimization techniques.