Hey, I am Rodrigo Carreira

Developed and deployed AI, ML, and GenAI solutions across internal TuplOS tools and enterprise services, integrating LLMs, RAG pipelines, and AI agents to automate workflows and enhance system intelligence. Designed and implemented an agentic framework supporting MCP, A2A communication, RAGs, and multi-agent orchestration, enabling the creation and deployment of custom AI agents for diverse use cases across the company and partner ecosystems.

As a qualified software engineer, led the end-to-end development lifecycle of AI-driven systems, including architecture design, CI/CD pipeline implementation using Jenkins, and production deployment using Kubernetes and Docker. Designed scalable microservice architectures, created and maintained Helm charts for automated Kubernetes deployments, and implemented cluster monitoring and service management to ensure reliability and scalability. Applied modern software design patterns, optimized database integration, and collaborated cross-functionally to deliver robust, secure, and high-performance enterprise solutions aligned with business objectives.

Coauthored and published the SAES Python library for non-parametric statistical tests with researcher Antonio J. Nebro, enabling advanced statistical analysis and visualization. Conducted research on automatic code generation using Large Language Models (LLMs), integrating LangChain with frameworks such as jMetal and jMetalPy for multi-objective optimization.

Developed business process optimization solutions using machine learning (ML) and Graph Neural Networks (GNNs). Worked with Business Process Model and Notation (BPMN) tools such as Activiti, Bonita BPM, and Camunda. Applied my knowledge of artificial intelligence, focusing on machine learning and GNNs, while also utilizing genetic algorithms, neural networks, and optimization techniques.