• You will build the systems that make Alaro’s product meaningfully useful to lawyers: agents, extraction tools, and controllers that automate repetitive work and reliably execute delegated tasks. • At a high level: we are building a Cursor-like experience for legal documents and legal research - where an agent can search, reason over sources, and propose or apply edits while the lawyer stays in control. • This is an engineering-first applied AI role. To succeed, you’ll need to understand the full product surface (editor, retrieval, dashboard workflows), so you can spot where an agent should be given control - and ship it end-to-end. • Document editing: an agent proposes changes and generates/applies patches to DOCX documents, built for legal reality: multiple collaborators, tracked changes, strict formatting, and high expectations for correctness. • Search & retrieval: when a lawyer asks “search legislation” or “find similar contracts in our database,” the agent decomposes that into targeted queries, retrieves the right sources, and grounds outputs with citations. • Deep research workflows: solving legal problems requires iterative investigation, where agents make progress step by step while keeping their active context focused and manageable. • Training models: fine-tuning models becomes a necessity to optimize the above workflows. As internal data matures, the agent can learn from past interactions to become more intelligent.

• Own the AI product roadmap across servicing, collections, and compliance, driving from vision through execution. • Translate operational workflows and lender pain points into clear AI feature requirements and measurable outcomes. • Lead cross-functional teams (engineering, data, operations, legal/compliance) to design, build, and ship AI features. • Evaluate and integrate with third-party AI providers; own partner selection, pricing, integration strategy, and ongoing performance monitoring. • Work closely with legal and compliance teams to ensure all AI solutions meet regulatory, audit, and explainability standards. • Instrument AI features for performance measurement (accuracy, deflection, compliance rates, etc.) and drive continuous iteration. • Act as the internal evangelist for AI capabilities, providing product expertise to operations, customer success, and clients. • Stay ahead of emerging AI capabilities and vendor landscape to inform the roadmap.

• Develop and maintain GenAI-powered application features and workflows. • Integrate large language model APIs and generative AI capabilities. • Optimize system performance and ensure robust user experience. • Work with cloud platforms to deploy scalable AI solutions. • Conduct thorough testing and maintain clear technical documentation.

• Construir y desplegar aplicaciones Fullstack con IA (frontend + backend). • Ser dueño del ciclo completo: de la idea a producción 🚀. • Desarrollar infraestructura clave: search/retrieval, workflows, agentes inteligentes y MCP servers. • Crear sistemas de medición y feedback para mejorar continuamente el comportamiento de la IA 📊. • Resolver problemas complejos a lo largo de todo el stack.