AI Engineering Laboratory - Software with purpose
A crew of AI master's candidates developing, researching, and designing intelligent systems, agents, retrieval workflows, context engines, and frontier-level AI solutions from Lima.
Each profile represents a specialization. Together, they form a practical AI engineering crew across software, data, infrastructure, and applied intelligence.
Software developer specialized in React, Nest.js, Python, Azure, and the integration of frontier models and agents. Focused on user experience, performance, quality, architecture, continuous research, and teaching.
Systems engineer oriented toward software solutions and artificial intelligence research. Focused on technological innovation, software architecture, and scalable applications with strong analytical and collaborative skills.
Software engineer with a management-driven perspective, specialized in electronic security, access control, and artificial intelligence. Leads critical infrastructure, telecommunications, and enterprise solutions aligned with technology, scalability, and business goals.
Systems engineer specialized in databases, information analysis, and process optimization. Currently building solutions with PostgreSQL, Oracle, and artificial intelligence, combining analytical thinking, innovation, and continuous improvement.
Principles that keep the crew aligned across code, research, infrastructure, and learning.
“Prototype fast. Measure honestly. Share what compounds.”
Principle 01
We turn ideas into working systems, then harden them through real usage.
Principle 02
We study papers, systems, failures, and tools until the idea becomes operational knowledge.
Principle 03
We treat experiments, benchmarks, and technical writing as part of the product.
Principle 04
We document the path so the next builder starts from a higher floor.
Principle 05
We care about infrastructure, observability, reliability, and the practical details that make AI useful.
A map of the systems, data workflows, and AI engineering questions currently open in the lab.
Case studies from the master's program where data engineering, backend architecture, and applied AI practices become usable software.
data-pipeline
Problem
Process and expose millions of music records efficiently for fast analytical queries and REST consumption.
Solution
Built a complete data pipeline using MinIO, SingleStore, Parquet, Apache Zeppelin, and FastAPI to ingest, optimize, analyze, and serve Discogs data through REST APIs.
16.3M+
records processed
hexagonal-architecture
Problem
Provide a maintainable backend for managing students and enrollments following modern software architecture principles.
Solution
Developed a Spring Boot REST API with Hexagonal Architecture, OpenAPI documentation, and deployment-ready configuration for academic management.
OpenAPI
documented CRUD
A focused set of tools across AI engineering, data platforms, backend systems, cloud infrastructure, and product interfaces.
Search sections, projects, research, and links.