AI Engineer

• Definir y mantener la arquitectura técnica de la plataforma y sus servicios
• Diseñar abstracciones LLM-agnostic con routing, fallback, budgets y observabilidad uniforme
• Determinar qué tareas deben resolverse con modelos y cuáles mediante código, compiladores, schemas o workflows determinísticos
• Diseñar agentes confiables, acotados, auditables y capaces de operar con herramientas externas
• Evolucionar la integración MCP, el catálogo de capacidades y los gateways de ejecución
• Diseñar modelos seguros de identidad, consentimiento, credenciales, permisos, cuotas y revocación
• Garantizar idempotencia, trazabilidad, aislamiento y manejo correcto de reintentos y fallas parciales
• Diseñar pipelines reproducibles de build, verificación, despliegue y rollback con artefactos inmutables
• Crear eval suites y pruebas end-to-end para prompts, modelos, tools, seguridad y runtime
• Definir estándares de seguridad para código generado, dependencias, contenedores, networking y supply chain
• Investigar incidentes end-to-end y corregir causas estructurales
• Diseñar SLOs, métricas, alertas y dashboards operativos
• Liderar revisiones de arquitectura y código, documentar decisiones y acompañar técnicamente al equipo
• Evaluar modelos, frameworks y proveedores mediante evidencia, benchmarks y análisis de costo total
• Desarrollar plataformas de aplicaciones y agentes de inteligencia artificial para Cashea, empresa venezolana de inclusión financiera y BNPL

• Build, deploy, and maintain internal AI agents and intelligent search tools using organizational documents and knowledge
• Automate repetitive business workflows with agentic systems executing multi-step autonomous actions across internal tools
• Extend back-office platforms such as Monday.com, Microsoft 365, SharePoint, Ironclad, and other SaaS tools through custom integrations
• Automate document review, comparison, summarization, data extraction, classification, and creation
• Build templates, prompts, and workflows for RFPs, RFIs, proposals, reports, and presentations
• Design and deploy lightweight internal applications, web tools, and scripts
• Identify high-impact automation opportunities and evaluate AI features during software procurement
• Determine when to build custom tools versus buying existing solutions
• Establish reusable AI infrastructure, tool-calling patterns, permissions, logging, security frameworks, evaluation methods, and organizational AI best practices
• Collaborate with business teams and leadership to rapidly prototype solutions
• Train, support, and enable employees to use AI tools effectively

• Design and develop reusable components, libraries, APIs, plugins, and tools that enable AI adoption across products and engineering teams
• Integrate AI capabilities into the internal development framework and make them easy for engineering teams to adopt
• Define reference architectures, technical patterns, standards, and best practices for Generative AI-based solutions
• Design and implement integrations with Large Language Models and AI services, primarily within the AWS ecosystem
• Develop production-ready solutions using Node.js, TypeScript, serverless architectures, and the Serverless Framework
• Explore and implement structured generation, tool calling, agents, RAG, MCP, embeddings, and semantic search
• Define testing and evaluation mechanisms for quality, security, latency, reliability, and cost of AI-powered solutions
• Implement observability for model usage, prompts, tokens, responses, errors, execution times, and other operational metrics
• Establish technical controls and engineering practices for secure and responsible AI usage
• Build proof-of-concepts and evolve successful approaches into scalable, maintainable, production-ready solutions
• Create technical documentation, implementation examples, and adoption guidelines
• Support development teams in adopting reusable AI components, architectures, and engineering best practices
• Participate in architecture reviews and contribute to technical decision-making
• Share knowledge and promote AI engineering best practices