Santiago – Node.js, AWS, Typescript

B2B marketplace connecting veterinary clinics with pharmaceutical vendors in the USA, replacing a legacy Visual Basic + Access workflow with a centralized, modern web platform for pricing and purchasing.

• Led the migration from a legacy Visual Basic + Access system to a modern web platform.​
• Designed and implemented the frontend and backend architecture using Next.js, Prisma, and PostgreSQL.​
• Built a data ingestion pipeline to normalize legacy Excel/Access datasets.​
• Imported external datasets from multiple vendors and consolidated them into a unified product catalog.
• Implemented data analytics workflows to generate reports and dashboards for pricing, sales, and product performance.
• Exposed analytics data through secure APIs for internal stakeholders.
• Developed core product flows in Next.js, including vendor catalogs, pricing logic, and ordering.​
• Collaborated with stakeholders to translate complex business rules into scalable product features.​
• Set up CI/CD pipelines and infrastructure using Vercel, Docker, and AWS services.​
• Optimized database schema and queries to improve pricing and product performance.​
• Mentored developers and established code quality and architectural standards.

MCP STM Montevideo is a developer tool that exposes Montevideo public transportation data through the Model Context Protocol (MCP), making it usable by AI assistants and agent-based applications.

The project was built to let LLM-powered clients answer real-world transit questions in natural language, such as how to travel between two points, which bus lines are available, or where nearby stops are located.

Its main value is bridging public transport data and AI tooling through a clean server interface that can be consumed by MCP-compatible clients such as Claude Desktop, Cursor, or custom agent workflows.

Main features:

• exposes STM transport data as MCP tools;
• supports queries about routes, stops, and trip options;
• enables natural language transport assistance for Montevideo;
• provides a practical example of integrating public infrastructure data into AI ecosystems.

• Designed and built the MCP server architecture from scratch using Node.js and TypeScript;
• Modeled transport-related capabilities as tools consumable by AI assistants;
• Integrated public transportation data into a format suitable for conversational and agent-based use cases;
• Focused on clean developer experience, maintainability, and practical real-world usefulness;
• Validated the project as an example of how public infrastructure data can be turned into AI-ready tooling.

Backend platform for a neurostimulation device focused on improving sleep quality. The system supports device communication, user management, subscriptions, and data processing at scale.

• Designed the backend architecture and AWS infrastructure from scratch.
• Implemented Terraform-based infrastructure for ECS, RDS, S3, and networking.
• Designed and implemented MongoDB data models for device telemetry and user interaction events, optimizing read-heavy workloads through compound indexing and aggregation pipelines.
• Built a TypeScript Express API with Prisma and PostgreSQL (partitioned tables).
• Implemented background workers using BullMQ to offload heavy processing.
• Integrated third-party APIs for subscriptions and user management.
• Set up CI/CD pipelines and Dockerized the entire system.
• Wrote Unit and integration tests to ensure system reliability and scalability

Financial platform modernization project focused on improving performance and maintainability.

• Refactored and migrated a legacy Laravel backend to Node.js.
• Improved system performance and stability through targeted refactors.
• Conducted code reviews and enforced best practices.
• Introduced event-driven components using AWS Lambda to decouple critical backend workflows and improve system scalability.
• Implemented asynchronous processing pipelines leveraging AWS SQS and SNS to handle background jobs and cross-service notifications reliably.
• Mentored junior developers during the migration process.

Backend system for a veterinary appointment scheduling and health data management application.

• Designed backend architecture and AWS infrastructure.
• Implemented CI/CD pipelines and automated deployments.
• Debugged and resolved performance and reliability issues.
• Provided technical guidance to junior developers during development.

Backend system for a drug data management platform used to handle pharmaceutical information and structured medical datasets. The application supports data processing, validation, and API-based access to drug-related data.

• Developed backend features using Nest.js and TypeScript.
• Implemented and maintained REST APIs for structured drug data access.
• Worked with PostgreSQL to ensure data consistency and performance.
• Wrote unit tests to improve reliability and prevent regressions.
• Collaborated with cross-functional teams to deliver features aligned with business requirements.