• Liderar la ejecución de soluciones empresariales de alta complejidad • Asegurar la excelencia en ingeniería, supervisando la implementación de sistemas robustos • Garantizar la resiliencia en servidores de aplicaciones críticos • Orquestar equipos de alto rendimiento bajo estándares internacionales de calidad y metodologías ágiles

• Design and develop large scale distributed software services and solutions • Adhere to and drive modern software engineering practices through design and code reviews • Develop "best-in-class" engineering for our services by ensuring that the services and the components are well-defined and modularized, secure, reliable, diagnosable, actively monitored and reusable • System design through well-defined interfaces across multiple components, code reviews, leveraging data/telemetry to make decisions • Improve test coverage for services, organize and implement integration tests, and resolve problem areas • Consider testability, portability/monitoring, reliability, and maintainability, and understand when code is ready to be shared and delivered • Gain a working understanding of Luware as a business and collaborate with mentors and leaders to contribute to cohesive, end-to-end experiences for our users • Focus on customer/partner needs through a data driven approach

• Design and build core platform primitives including provider abstraction layers (OpenAI, Anthropic, Google), structured output validation, streaming infrastructure, and token management systems • Own safety and compliance infrastructure including composable guardrail systems, PII detection/redaction, audit logging, and privacy-first observability that never leaks sensitive data to third parties • Build evaluation infrastructure that enables systematic quality measurement for non-deterministic LLM outputs—datasets, scorers (exact match, LLM-as-judge, schema validation), CI/CD integration, and regression detection • Lead churn containment strategy—design provider adapters and SDK architecture that absorbs rapidly-changing LLM provider SDKs without breaking consuming applications • Architect prompt lifecycle management systems including version control, Langfuse integration, GitHub-based review workflows, and deployment pipelines • Design Agent-as-a-Service infrastructure for long-running async tasks using AWS EventBridge, DynamoDB, and PostgreSQL • Collaborate with consuming teams to understand their needs, onboard them to the platform, and provide technical support • Influence architecture, technology selections, and engineering standards across the broader organization • Create reference implementations and technical documentation that enables other engineers to successfully adopt the platform • Champion quality engineering practices including comprehensive testing, type safety, and observability

• Architect and build the foundational LLM architecture (RAG, Agents, Vector DBs) that powers high-impact product features across the organization. • Design and deliver high-performance, distributed systems (data pipelines, APIs, microservices) that process healthcare data at scale and securely integrate LLM outputs into the user experience. • Drive Proof-of-Concepts (POCs) and technical evaluation of new AI technologies to validate their product viability and scalability. • Design and build responsive web applications using modern frontend frameworks to deliver intuitive, user-facing intelligence and analytic features. • Ensure observability, monitoring, and operational excellence, championing security and regulatory compliance (HIPAA, SOC2). • Drive system design and technical planning for all critical projects, including large, complex, and cross-functional initiatives across the organization. • Partner strategically with Product, Operations, and Design leadership, translating complex business requirements into technical strategies and influencing roadmap prioritization. • Mentor engineers at Software Engineer I, II, and III levels, accelerating their technical growth and leadership development. • Educate the organization on the capabilities, best practices, and limitations of using LLMs and applied AI within the healthcare domain. • Define and implement engineering standards, patterns, and best practices that significantly improve scalability, reliability, and developer productivity across the product.