• Develop, optimize, and maintain backend applications for scalable platforms • Implement APIs and backend logic for web and mobile applications • Work on database design, query optimization, and system performance improvements • Collaborate with frontend developers, AI engineers, and DevOps teams to deliver integrated solutions • Continuously learn and improve backend best practices

• Build fluid, responsive user interfaces and resilient backend services for multiple platforms • Solve complex technical problems to world-class standards • Embedded in a team responsible for the full software delivery lifecycle • Work on asynchronous, event-driven workflows and modern cloud-native services • Contributing to a codebase that prioritizes performance, security, and resilience

• Design and implement solutions in Python using the full suite of AWS cloud computing capabilities • Develop and iterate on AI and LLM-based workflows to reduce overhead and time for customers to achieve their required outcomes • Operate in a collaborative, agile environment with a focus on taking action and enabling team success • Create proofs-of-concept, prototypes, and other solutions to quickly test ideas and enable data analysis, as well as design and build production solutions • Help us achieve and maintain compliance with information security best practices • Engage with peers across the company to review and help ensure our team delivers maintainable and extensible solutions • Share your knowledge with, and learn from your colleagues in all parts of the company

• Design and build highly available, scalable cloud applications end to end • Develop new features and microservices within our existing infrastructure • Build and own observability infrastructure for our AI systems — accuracy dashboards, performance monitoring, anomaly detection, and alerting on prediction quality • Design and maintain evaluation pipelines that continuously measure model accuracy across document types, edge cases, and customer environments • Define and track key performance metrics — precision, recall, confidence thresholds, drift indicators — and turn them into actionable engineering work • Instrument our services with structured logging, tracing, and metrics collection to ensure full visibility into how our AI behaves in production • Improve and refactor existing services for performance, reliability, and maintainability • Write clean, testable, well-documented code and participate in thoughtful code reviews • Collaborate closely with product, data science, and fellow engineers to ship meaningful improvements • Contribute to engineering practices, tooling, and architecture decisions