• design, develop, and deploy scalable full stack applications and microservices • own and deliver key features end-to-end • write high-quality, maintainable code • modernize legacy systems • troubleshoot and resolve complex production issues • participate in a shared on-call rotation

Role Description - Technical Leadership (40%) - Set technical direction and standards for ML projects - Make architectural decisions for ML systems - Review and approve technical designs - Identify and address technical debt - Champion best practices in ML engineering - Troubleshoot complex technical challenges - Evaluate and introduce new technologies and tools - Mentorship & Team Development (35%) - Mentor junior and mid-level ML engineers (2-5 engineers) - Conduct technical code reviews - Provide guidance on technical problem-solving - Help engineers debug complex issues - Create learning opportunities and growth paths - Share knowledge through workshops and documentation - Build technical competency across the team - Hands-On Technical Work (25%) - Contribute code to critical or complex components - Build proof-of-concepts for new approaches - Tackle highest-risk technical challenges - Develop reusable ML accelerators and frameworks - Maintain technical credibility through active coding Qualifications - ML Engineering Excellence - Deep ML Expertise: Advanced knowledge across multiple ML domains - Production ML: Extensive experience building production-grade ML systems - Architecture: Ability to design scalable, maintainable ML architectures - MLOps: Strong understanding of ML infrastructure and operations - LLM Systems: Experience with modern LLM-based applications and RAG - Code Quality: Exemplary coding standards and best practices - Technical Breadth - Multiple ML Frameworks: Proficiency across TensorFlow, PyTorch, scikit-learn - Cloud Platforms: Advanced AWS experience, familiarity with others - Data Engineering: Understanding of data pipelines and infrastructure - System Design: Ability to design complex distributed systems - Performance Optimization: Experience optimizing ML models and infrastructure - Software Engineering - Clean Code: Writes exemplary, maintainable code - Testing: Champions testing practices (unit, integration, ML-specific) - Git & Collaboration: Advanced Git workflows and collaboration patterns - CI/CD: Experience building and maintaining ML pipelines - Documentation: Creates clear, comprehensive technical documentation Benefits - Long-term B2B collaboration - Fully remote setup - A budget for your medical insurance - Paid sick leave, vacation, public holidays - Continuous learning support, including unlimited AWS certification sponsorship Interview Stages - Recruitment Interview - Tech interview - HR Interview - HM Interview We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

• Liderar la evolución técnica de Mattilda Pay, desde la arquitectura hasta la implementación • Colaborar con el Product Owner para alinear prioridades técnicas con las necesidades del producto • Fortalecer los flujos de pagos, facturación y conciliación en México, Colombia y Ecuador • Impulsar la migración hacia arquitectura orientada a eventos y mejorar la consistencia de datos en el dominio de pagos • Establecer estándares técnicos y el nivel de calidad de código en el equipo

• Integrarse a un equipo de ingeniería enfocado en el desarrollo de plataformas cloud-native y sistemas distribuidos de alto impacto. • Liderar iniciativas complejas de ingeniería.