• Build and maintain frontend applications using Next.js 14+ (TypeScript) with multi-tenant routing • Develop backend services using Node.js (TypeScript) deployed via AWS Lambda • Ensure strong type safety and consistency across the full stack • Contribute to scalable, maintainable architecture across distributed systems • Design and maintain a dedicated integration layer using AWS Lambda + SQS • Integrate external platforms (e.g., gym systems, CFMS, Speedy Sticks, CommunityCare) • Implement retry logic, failure handling, and system resilience • Maintain clear separation between integration services and application APIs • AWS API Gateway in front of the integration layer Lambdas provides throttling, request validation, and API key management across third-party service endpoints • Treat AI-assisted development (e.g., Cursor, GitHub Copilot, or similar tools) as a baseline capability • Apply thoughtful prompt engineering and structured workflows to produce reliable, maintainable code • Use AI tools across the development lifecycle, including design, implementation, testing, and documentation • Stay current with the evolving AI tooling landscape and adopt practices that improve developer productivity • Build and maintain infrastructure using AWS Lambda, SQS, IAM, Cognito, Amplify, and RDS • Deploy frontend applications via AWS Amplify, Fargate or similar • Design systems that are scalable, cost-efficient, and operationally simple • Work with PostgreSQL (AWS RDS) using relational modeling • Implement row-level security for multi-tenant data isolation • Manage authentication and authorization using AWS Cognito (RBAC, JWT scoping) • Implement file storage using AWS S3 (e.g., lab results, CSV uploads) • Build notification workflows using AWS SES • Integrate and maintain payment systems using Stripe • Implement polling-based updates (30-second intervals) for journey tracking • Design user experiences that feel real-time while avoiding unnecessary infrastructure complexity • Own release cuts, including PR reviews and deployment coordination • Manage environment configurations, secrets, and package upgrades • Debug and resolve complex system and integration issues • Build proofs of concept (POCs) to validate new features and approaches • Lead or support Agile ceremonies (standups, sprint planning, retrospectives) • Partner with Product Managers to refine and deliver epics and requirements • Act as a technical point of contact within the pod and across collaborating teams • Provide guidance and context to teammates to support delivery and quality

• Define, design, and implement new software applications in collaboration with other members of the development and technical teams, based on industry standards and best practices. • Translate application storyboards and use cases into functional applications. • Perform Test-Driven and Security-Driven Development. • Conduct comprehensive code reviews and ensure that coding standards are followed. • Analyze call stacks, trace files, debug logs or performance data to troubleshoot and identify the root cause of bugs. • Participate in development of prototypes, minimum viable products, and PoCs. • Help maintain code quality, organization, and automation. • Perform maintenance programming for existing applications. • Consider the functionality, performance, scalability, reliability, realistic implementation schedules and system architecture for each assignment. • Provide design documents and documentation to ensure efficient maintenance and code reuse. • Provide recommendations for improvements in the overall development process. • Participate in a 24x7 on-call rotating schedule.

• Technical responsibility for SAP Basis architectures • Operation and ongoing development of complex SAP system landscapes • Management of projects and assumption of team leadership • Planning and implementation of transformations towards SAP S/4HANA • Integration of SAP systems into cloud platforms such as Microsoft Azure • Analysis of existing SAP landscapes and development of strategies

• Develop and architect AI agent systems based on single-agent and multi-agent architectures. • Implement modern communication protocols such as Model Context Protocol (MCP) and Agent-to-Agent (A2A). • Master the information lifecycle for AI: from strategies like chunking and embeddings to implementing techniques such as RAG (Retrieval-Augmented Generation), reranking, and query transformation. • Manage the complexity of multi-model environments. Evaluate and select the best providers based on trade-offs in latency, context, and cost. Standardize the use of abstraction frameworks to avoid vendor lock-in. • Act as a technical mediator in defining AI strategies across the product portfolio. • Ensure architectural consistency across initiatives, identifying opportunities for componentization and reuse. • Serve as a decision-maker in architectural dilemmas, promoting an AI Engineering culture. • Connect AI technical capabilities to the real pain points of business areas. Establish LLMOps pipelines that include: • Rapid experimentation cycles (A/B testing of prompts/models) • Feedback systems (human-in-the-loop) • Monitoring of business metrics impacted by AI • Systematic evaluation (Evals) - creation of test datasets and frameworks.