Software engineer/AI engineer, IES LATech & GIS

Amazon.com's long-term vision is to enable a seamless global shopping experience for all Amazon customers worldwide on any Amazon website or device, in any language and for any culture. Our goal is to create software that offers customers a unique shopping experience that is internationalized for their preferences. Our team is responsible for automating the launch strategy for Amazon's expansion into new countries. We build customer-facing features in a highly distributed environment that directly impacts Amazon’s e-commerce revenue in over 150+ countries.

We’re looking for an experienced Software Engineer with AI background to join our team in building GenAI-powered tools to solve real problems that require creative and deep understanding of the problem space. The ideal engineer for this space will be highly quantitative, has great judgment and passion for building a great customer experience, be inventive, and have a strong track record of delivery. You also have a pragmatic approach and iterative approach to building software: you have an ability to simplify and get things done with a demonstrated track record of building and delivering software and working effectively with external and internal teams.

Key job responsibilities

• Design and implement frameworks and solutions that enable seamless integration of GenAI technologies into existing products and development workflows enhancing productivity
• Collaborate with engineering and applied scientists teams to identify opportunities for GenAI-driven automation and efficiency improvements
• Establish best practices and guidelines for GenAI development and usage across the organization
• Develop reference architectures and technical guidelines for GenAI feature integration
• Drive adoption of GenAI solutions through technical demonstrations, workshops, and documentation
• Collaborate with teams across Amazon to share learnings and stay current with GenAI developments
• Evaluate and optimize the performance, security, and cost-effectiveness of GenAI-powered solutions