• 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

• Build, maintain, and improve full-stack features using Vue.js, TypeScript, Laravel, and MySQL. • Develop robust APIs and integrate with external services. • Write clean, maintainable, and tested code (unit/integration/e2e where relevant). • Optimise performance across frontend, backend, and database queries. • Collaborate closely with product, design, and engineering teammates in an agile environment. • Support CI/CD workflows and production reliability (monitoring, queues, background jobs, caching, etc.).

• Design reproducible software engineering environments for evaluating AI model performance. • Develop high-quality reference ("golden") solutions for complex programming challenges. • Contribute expert-level code samples in one or more of the following languages: - Python - Java - Rust - Go - C++ - TypeScript • Analyze, debug, and resolve complex software defects across diverse codebases. • Design, implement, and validate scalable software features following industry best practices. • Refactor legacy applications to improve maintainability, readability, and performance. • Optimize software systems by identifying bottlenecks and improving efficiency. • Document technical reasoning, implementation decisions, and solution methodologies to improve AI training quality. • Review and validate technical submissions, ensuring correctness, clarity, and engineering excellence. • Collaborate remotely with cross-functional teams to continuously improve project quality and deliverables.

• Transform business logic into high-quality code, adhering to established standards and practices; • Participate in defining and implementing new features and fixing bugs; • Understand and discuss issues and proposed solutions, always seeking the best technical solution for encountered challenges; • Review code delivered by team members; • Break down large deliveries into small, cohesive deliverables; • Monitor the deployed solution in production, evaluating errors, performance, and availability; • Collaborate on improvements or new standards in the Engineering knowledge base.