Nooxit

• Design and build highly available, scalable cloud applications end to end • Develop new features and microservices within our existing infrastructure • Build and own observability infrastructure for our AI systems — accuracy dashboards, performance monitoring, anomaly detection, and alerting on prediction quality • Design and maintain evaluation pipelines that continuously measure model accuracy across document types, edge cases, and customer environments • Define and track key performance metrics — precision, recall, confidence thresholds, drift indicators — and turn them into actionable engineering work • Instrument our services with structured logging, tracing, and metrics collection to ensure full visibility into how our AI behaves in production • Improve and refactor existing services for performance, reliability, and maintainability • Write clean, testable, well-documented code and participate in thoughtful code reviews • Collaborate closely with product, data science, and fellow engineers to ship meaningful improvements • Contribute to engineering practices, tooling, and architecture decisions

• You'll be the sole product designer on the team, working closely with our product manager and engineering team to shape interfaces that are used daily by finance professionals across Europe. • The scope is wide: from AI-powered document processing flows to compliance dashboards to agentic task monitoring. • This is a high-ownership role at an early product stage. • You won't be handed a finished design system and asked to fill in screens - you'll help define how things should work, and why. • AI interaction design: designing for agentic workflows, automation confidence, and human-in-the-loop controls where AI acts but users stay informed • Complex data surfaces: dashboards, review queues, audit trails, and exception-handling flows for finance teams managing large document volumes • Design system ownership: building and maintaining a component library that scales across our product surfaces • Discovery and validation: running user research sessions, usability tests, and stakeholder workshops (in English; German a bonus) • Cross-functional collaboration: embedded in the product and engineering team from ideation through to shipped feature • AI tooling in your workflow: using generative and AI-assisted design tools to move faster and prototype more freely

• Design and build highly available, scalable cloud applications end to end • Develop new features and microservices within our existing infrastructure • Build and own observability infrastructure for our AI systems — accuracy dashboards, performance monitoring, anomaly detection, and alerting on prediction quality • Design and maintain evaluation pipelines that continuously measure model accuracy across document types, edge cases, and customer environments • Define and track key performance metrics — precision, recall, confidence thresholds, drift indicators — and turn them into actionable engineering work • Instrument our services with structured logging, tracing, and metrics collection to ensure full visibility into how our AI behaves in production • Improve and refactor existing services for performance, reliability, and maintainability • Write clean, testable, well-documented code and participate in thoughtful code reviews • Collaborate closely with product, data science, and fellow engineers to ship meaningful improvements • Contribute to engineering practices, tooling, and architecture decisions