• 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.

• Ship modernization work at a pace that surprises people • Build reusable playbooks the team can actually follow • Modernize a real .NET + SQL Server stack into something you'd be proud of • Own it like it's yours - especially because this is healthcare • Work with real humans to ship things that matter

• Lead the development of scalable software systems that integrate generative AI models, autonomous agents, and machine learning pipelines • Collaborate with cross-functional teams to identify opportunities for AI-first innovation across digital health products • Drive the adoption of AI engineering best practices, including prompt engineering, model fine-tuning, and responsible AI governance • Facilitate technical discussions between Application Architects and developers to ensure AI capabilities are embedded into the design and architecture of new solutions • Stay current with advancements in AI and emerging technologies, and translate them into actionable strategies for product development • Design, develop, and maintain scalable data pipelines and APIs using Python and FastAPI • Integrate and orchestrate LangChain and LLM-based services into production environments • Build robust data preprocessing workflows for structured and unstructured data • Develop and maintain integrations with external and internal RESTful services • Collaborate with ML engineers, data scientists, and product teams to deliver end-to-end AI solutions • Implement DevOps best practices including CI/CD pipelines and containerization (Docker) • Identify and resolve security vulnerabilities in data services and APIs • Monitor and optimize system performance, reliability, and scalability