• Own major components of a new SaaS platform from design through production • Build event-sourced, CQRS-based services in C#/.NET using DDD principles • Design clean domain models, message contracts, and integration patterns using RabbitMQ • Deliver cloud-ready solutions using Docker and AWS (deployment, observability, reliability) • Apply an AI-first approach to engineering: agentic AI tools, automated workflows, code generation/review, test generation, and productivity accelerators • Partner with product and engineering leaders to define architecture, standards, and delivery practices • Write high-quality, well-tested, maintainable code and raise the bar through reviews and mentorship

• Develop and modernize software applications using best practices in coordination with a distributed team • Have an advanced understanding of software development methodologies and a proven ability to design and build complex software systems • Contribute to design decisions where needed • Participate in all aspects of an agile software development process, including design, coding, testing, documentation, quality assurance, and deployment • Leverage "innovation time" to work on personal projects and special areas of interest

• Lead greenfield MVP development and architect the initial V1 product • Implement a flexible, white-label React application • Build a backend to relay data into the white-label solution and internal data systems • Deliver a secure, scalable, and auditable trust accounting subsystem with high-quality standards • Help refine early processes to improve delivery speed, system reliability, and team autonomy • Champion engineering practices that emphasize quality, resilience, and observability • Provide technical context in discussions and decisions, fostering transparency and accountability • Establish initial frontend CI/CD pipelines to support independent deployments

• Design and build services that integrate AI into customer-facing features. • Develop performant and scalable APIs and backend services that connect AI outputs to SaaS functionality. • Collaborate with product and design teams to create intuitive AI-driven user experiences. • Partner with platform and integration teams to embed AI into existing systems and workflows. • Implement observability, monitoring, and error handling for AI services in production. • Contribute to evolving our AI integration stack (Python SDKs, MCP servers, LLM orchestration).