• Design end-to-end AI solution architectures for enterprise environments • Lead the technical design of Generative AI and Machine Learning solutions • Build production-ready AI services using Python • Define AI platform architecture, integration patterns and deployment strategies • Design scalable RAG (Retrieval-Augmented Generation) solutions • Select appropriate LLMs, vector databases and AI frameworks • Collaborate with Software Engineers, Data Engineers, Data Scientists and business stakeholders • Define security, governance and responsible AI best practices • Review solution designs and mentor engineering teams • Drive AI engineering standards and best practices across projects

• Design and develop AI-powered applications using Python • Build production-ready LLM integrations and AI services • Develop RAG pipelines and knowledge retrieval solutions • Integrate external APIs, enterprise systems and AI models • Create reusable AI components and backend services • Optimize prompts, inference performance and application quality • Develop automated testing for AI applications • Collaborate with Architects, Data Engineers and Software Engineers • Participate in code reviews and technical improvements • Contribute to CI/CD and engineering best practices

• Desarrollar soluciones de software utilizando Java y Python. • Trabajar con arquitecturas de microservicios. • Implementar soluciones en el entorno de AWS. • Colaborar con equipos multidisciplinarios para entregar proyectos de tecnología.

• Build and maintain scalable backend services using Python (Flask/FastAPI) that process data from 300,000+ IoT devices • Design and develop REST APIs consumed by internal tools and customer-facing applications • Build and improve React/TypeScript frontend features — from translating designs into code to refining existing UI components • Write database queries to analyze how features are actually being used and to debug production issues • Build deployment monitoring and diagnostic tools that give visibility into system health across geographies • Create tools that provide transparency and ease of access into the company's rich datasets stored across varying geographic locations and data formats • Collaborate with engineering teams to ensure data persists in usable formats for frontend, middleware, diagnostics, and Deep Learning pipelines • Remotely debug customer issues across the full stack and find smart solutions that actually work • Partner with the product team to shape user experience — you'll bridge the gap between backend capabilities and what users actually see • Support our customer success team by helping solve client problems • Evaluate incoming requests — assess the effort required vs. potential value delivered • Expand test coverage for critical backend services and frontend user flows • Refactor existing code to reduce complexity and keep things maintainable • Be part of the team that ships weekly releases to customers • Present your work to the company and share what you've shipped