Hi, I'm
Software & Industrial Engineer | AI Enthusiast
Architecting mission-critical systems and high-performance AI ecosystems through Systems Thinking and Operations Research.
I am a Software and Industrial Engineer specializing in the architecture of high-scale distributed systems and AI-driven ecosystems. By integrating Industrial Engineering principles, I treat software development as a rigorous optimization process where efficiency, scalability, and long-term reliability are engineered into the foundation.
I specialize in building high-performance, mission-critical software where excellence is the baseline. My approach combines the versatility of a full-stack architect with the precision of a research-driven engineer, ensuring that every system I design exceeds strict functional requirements while maintaining world-class standards for security and reliability.
Nebula Medical
August 2025 – February 2026
AI Engineer / Backend Engineer for a medical intelligence platform, developing AI models with leading LLMs, building RAG systems, and creating data pipelines for healthcare workflows.
Caseware
June 2024 – July 2025
Contributed to a global audit platform serving 475,000+ users across 130 countries, developing integrations in the imports/bindings module, backend microservices, and modern frontend architectures while leading technical migrations and agile ceremonies.
Universidad de los Andes
Bogotá, Colombia
Jan 2021 – June 2025
GPA 4.21 / 5.0
Relevant Courses
Universidad de los Andes
Bogotá, Colombia
Jan 2022 – June 2025
GPA 4.21 / 5.0
Relevant Courses
A high-performance local search engine that indexes content across GitHub, Notion, Slack, and local files into a unified vector database. Includes an MCP server implementation that optimizes AI agent workflows by providing semantic search capabilities over local data.
An end-to-end ML platform that transforms talent acquisition by replacing keyword matching with semantic fit. It features multi-modal document understanding, a learned skill taxonomy via Knowledge Graphs (Neo4j), and contrastive candidate-job matching using Graph Neural Networks (GNN). Orchestrated through a production-grade MLOps pipeline with LLM-powered agents for explainable scoring and reasoning.
A modular Python library for generating professional CVs in DOCX and PDF format.
ML pipeline that implements temporal features from historical match data, trains and calibrates ensemble models (Random Forest, XGBoost, LightGBM), and sizes bets using the Kelly Criterion to exploit pricing inefficiencies in sports betting markets.
Real-time financial market alert system using WebSocket technology. Implements event-driven architecture with Redis for message brokering and PostgreSQL for data persistence. The system ensures low-latency data delivery for time-sensitive financial operations.
Developed predictive models for complex biological data using machine learning techniques. Applied advanced preprocessing (PCA), class balancing, and Deep Learning (TensorFlow/Keras) to achieve 98% accuracy in predicting complex patterns.
Developed a high-performance optimization system in Java and C++ for Electric Vehicle charging infrastructure. The system leverages multi-objective algorithms to minimize energy costs while maximizing customer service levels, resulting in a production-ready scheduling model.
A comprehensive management platform for facility administration, streamlining operations, records management, and resource allocation to improve operational quality.
Architected a scalable distributed system using Java Spring Cloud (Eureka, Config, Gateway) within an Nx monorepo to centralize dependency management. Implemented asynchronous messaging via RabbitMQ and resilience patterns with Resilience4j, integrating JWT security and Zipkin for distributed tracing.
Engineered deep learning architectures for high-dimensional biological data. Leveraged PCA for dimensionality reduction and Keras/TensorFlow for model development, achieving 98% accuracy in complex pattern recognition.
Showcased undergraduate thesis findings at the 2025 Analytics Forum, presenting advanced machine learning applications in bioinformatics to an audience of industry experts and researchers.
speaker at the Center for Optimization and Applied Probability (COPA) to discuss the integration of neural networks and probabilistic modeling in biological systems.
Data-focused expertise across machine learning, statistical modeling, LLM applications, data pipelines, and production-ready AI systems.