• Design, build, deploy, and maintain highly resilient and scalable features across Toast’s enterprise management products. Hands-on design and implementation experience in Java or Kotlin. • Lead complex projects that improve functionality and reliability for our customers • Partner with a wide array of teams across Toast • Ensure quality by building automated testing for changes • Roll features out to customers, including monitoring for success • Collaborate with UX and PM on the definition and design of features • Mentor and coach other engineers

• Deliver features end-to-end across our TypeScript/Node.js stack • Work on greenfield projects requiring creative, first-principles thinking • Build AI-powered features including chat, content generation, personalisation, and moderation • Leverage AI coding tools such as Cursor, Claude Code, and Copilot to accelerate development • Experiment with LLMs, embeddings, and AI integrations within product workflows • Ship quickly, iterate based on feedback, and learn from production • Collaborate closely with Product, Design, and senior engineers • Contribute to technical discussions and engineering standards

• Design, develop, and maintain AI-powered features and integrations across our platform, leveraging AWS Bedrock and related AWS AI/ML services. • Evaluate and select appropriate foundation models for specific business problems such as document analysis, intelligent matching, text extraction, and workflow automation. • Write maintainable, scalable, and well-tested code that integrates AI capabilities into existing application workflows. • Build and optimize prompt engineering strategies, retrieval-augmented generation (RAG) pipelines, and other techniques to maximize model accuracy and reliability. • Analyze, diagnose, and modify existing software to correct errors, adapt to new workflows, or improve performance. • Collaborate with product managers and stakeholders to identify processes that can benefit from AI automation and determine feasibility within time and cost constraints. • Establish best practices for AI integration including monitoring, cost management, evaluation frameworks, and responsible AI usage. • Work with other team members to create and evaluate estimates for development efforts based on stakeholder requirements. • Aid testers and support personnel as needed to troubleshoot the root cause of system issues. • Stay current with developments in AI/ML, foundation models, and AWS AI services, and advocate for their adoption where appropriate. • Additional responsibilities as assigned.

• Participate in shaping the technical vision for rebuilding our asset management platform from scratch • Design centralized data architecture with direct database integration and shared business logic reusable across SSR and future GraphQL. • Make critical architectural decisions including tech stack selection, state management patterns, data layer design, and performance optimization strategies. • Ensure future-proof architecture compatible with GraphQL migration while optimizing for immediate SSR performance • Define coding standards and best practices for the team, ensuring maintainable, scalable code • Collaborate with product and design teams to translate requirements into technical specifications • Build modern React-based apps with real-time capabilities using technologies like TanStack ecosystem, Server-Sent Events, and BroadcastChannel API • Build performant UI components with smooth data loading patterns, virtualization, and responsive user interactions • Optimize bundle size, lazy loading, and code splitting for <1s Time to Interactive • Build a shared business logic layer reusable across multiple service boundaries and rendering contexts • Design database schema and ORM integration • Develop background job processing systems for asynchronous workflows • Design and optimize performance-critical operations using systems-level programming and worker-based architectures to keep heavy processing off the main thread • Implement WASM modules for browser-based file processing with near-native performance • Build batch processing systems capable of handling large-scale concurrent operations efficiently • Profile and eliminate performance bottlenecks across critical data processing and transformation pipelines.