I am Luis Fernando Agamez,
a Software Engineer with a strong backend-oriented profile and over
2 years of hands-on experience in Software Development and AI.
I specialize in building scalable, well-structured systems while
continuously strengthening both my theoretical foundation and
practical execution capabilities. My experience allows me to understand
not only how software systems are built, but why engineering decisions
matter in real-world environments.
My background combines formal Software Engineering education with
applied technical training, providing a solid foundation in
core computer science principles, including
object-oriented programming, data structures, algorithms, databases,
software architecture, and system design. My training at
SENA further reinforced this knowledge through
hands-on development, disciplined coding practices, structured
problem-solving, and software planning under real-world constraints.
Beyond core development skills, I place strong emphasis on
technical communication, documentation, and software planning,
recognizing their critical role in professional engineering environments.
I prioritize clear technical communication, thoughtful requirement
analysis, and structured decision-making to build reliable,
scalable, and maintainable systems.
My work is openly documented on GitHub, where I
consistently publish projects, experiments, and production-oriented
implementations. I consider the
continuous creation and delivery of software
to be my strongest professional statement, as it reflects discipline,
sustained growth, and a strong commitment to engineering excellence.
I maintain a self-driven learning mindset, continuously
deepening my understanding of system behavior, modern backend practices,
and AI integration while building real-world solutions. I am committed
to designing and delivering robust technological systems that are
efficient, scalable, and aligned with professional engineering standards.
IntelliHub is a cloud-based AI assistant that performs calculations, greets users, and answers questions in real-time, powered by Groq AI and a modern web interface.
PythonAn AI voice assistant built with Python that integrates speech recognition, text-to-speech, persistent memory, and intelligent responses powered by Llama 3.3 via Groq. It includes task management, notes, reminders, contacts, and a FastAPI backend with SQLite storage.
PythonA full RAG system built from scratch combining a C++ binary vector store with real 384-dim embeddings, cosine similarity search, and LLaMA 3.3 70B generation via Groq API.
A real-time computer vision project powered by deep learning using MobileNetV2, featuring an interactive Streamlit interface. This showcases applied AI in action.
PythonRAG-based coding assistant using Groq LLMs, HuggingFace embeddings, and AstraDB to query GitHub READMEs, retrieve context, and answer questions. Features local note-taking and an interactive CLI.
PythonAdvanced MCP Server with real user authentication (Stytch), SQLAlchemy ORM, and full AI agent integration via the Model Context Protocol.
PythonAn end-to-end modular NLP system covering both training and inference, built with a clean and structured architecture. This project proves you understand how real-world NLP systems are designed and deployed.
PythonA production-oriented AI Gateway API built with FastAPI, designed to control and manage access to Large Language Models (LLMs) through a token-based credit system.
A real-world backend automation system that processes emails, generates PDFs, sends HTML emails, and integrates multiple scripts into a cohesive workflow. This is enterprise-grade backend engineering.
PythonSoftware engineering education focused on system design, programming fundamentals, algorithms, and professional software development.
Strong emphasis on C++ programming, object-oriented design, file handling, system methodologies, and logical problem solving.
