• Build and Maintain SaaS Products • Design, develop, and maintain scalable line-of-business SaaS applications • Build backend services, APIs, and data models • Ensure system performance, reliability, and security • Collaborate with product and business stakeholders to translate operational needs into software solutions • Integrate AI Capabilities • Leverage LLMs and AI APIs to enhance product functionality • Implement AI-driven features such as workflow automation, summarization, data extraction, classification, and intelligent search • Integrate AI into existing product architecture responsibly and efficiently • Monitor usage, performance, and cost of AI-powered features • Architect for Scalability • Contribute to service-oriented or microservice architectures where appropriate • Design clean API layers and integration patterns • Optimize database performance and data structures • Maintain observability and logging across systems Improve and Iterate • Continuously refine and enhance product capabilities • Refactor and improve existing systems as the platform grows • Ship features incrementally and iterate based on real-world feedback • Improve and Iterate • Continuously refine and enhance product capabilities • Refactor and improve existing systems as the platform grows • Ship features incrementally and iterate based on real-world feedback

• Design and build integration solutions that connect online patient intake, referral funnels, call center systems, and downstream clinical platforms • Develop and consume APIs to enable data flow across healthcare applications and services • Integrate external systems and services into EMR platforms, including Cerner or similar systems • Partner with product, clinical, and operational stakeholders to translate workflows into technical solutions • Support innovation initiatives involving AI-driven services, automation, and modern healthcare tooling • Ensure solutions meet healthcare security, privacy, and compliance expectations • Contribute to system design and architectural decisions while remaining hands-on in delivery • Operate comfortably in environments with evolving requirements and loosely defined problem spaces

• Design, develop, and deploy backend services and APIs that power Spotify's creator authorization systems. • Collaborate with different cross-disciplined teams on complex problems. • Maintaining business-critical Spotify authorization systems, ensuring high availability, stability, and adherence to SLAs. • Embrace and promote sound engineering practices and contribute to a culture of high-quality, scalable, and reliable systems. • Actively contribute to our learning culture by sharing knowledge and mentoring other engineers. • Leverage AI to improve our systems, automate tasks, and drive business impact.

• Design, build, and maintain solutions in a mix of Python and Typescript codebases, in combination with a low-code/no-code environment • Develop web applications using React or Vue with TypeScript and component libraries, focusing on clean architecture and reusability • Build and maintain RESTful APIs with proper error handling, validation, authentication, and security best practices • Implement data retrieval and transformation pipelines using Python/Pandas from relational databases (MySQL, PostgreSQL) • Develop data ontologies and maintain relational data architecture in collaboration with a team of data scientists • Design efficient data flows, considering cost and performance tradeoffs with big data • Collaborate with various stakeholders to produce well-designed technical solutions, clarifying ambiguous requirements and making informed technology/design decisions aligned with user needs • Write maintainable, well-documented code; participate in code reviews and adhere to team development standards • Deploy and troubleshoot solutions in multiple environments (Docker, AWS, etc.) • Stay current with emerging AI technologies and evaluate tools (OpenAI, Anthropic, Google) for product enhancement • Rapidly learn and adopt new technologies and frameworks as project needs evolve • Collaborate with data scientists on AI/ML algorithms for trend prediction, issue detection, and data categorization • Integrate LLM-powered features and ML models into product workflows