Full‑Stack + AI/ML Engineer building reliable, performance‑first products.

Systems that ship: clean UX, solid APIs, and production‑ready AI — with observability, security, and scale in mind.

FastAPINode.jsMongoDBPostgreSQLRabbitMQRedisDocker • CI/CD

Explore projects Experience

Featured projects

Curated highlights

Developer Productivity Visualizer

React-based visual tools to understand codebases faster across 500+ frontend engineers.

30% faster code comprehension

ReactTypeScriptD3Framer Motion

Interactive Code Maps

Zoomable architecture maps and dependency graphs that accelerate onboarding by 40%.

40% faster onboarding

Next.jsGraphTailwind

AI Docs Assistant Premium

Advanced AI-powered documentation search with premium chat features, streaming responses, and code execution.

92% user satisfaction + Enterprise features

OpenAI APIRAGStreamingCode SandboxTypeScriptAnalytics

Realtime Performance Profiler

Component-level metrics with flame graphs and actionable performance insights.

-25% bundle size via splitting

ReactWeb VitalsViteESBuild

Experience & Skills

Full‑Stack Developer

Agua

01/2023 - Present

Remote

Delivered an end‑to‑end React + FastAPI developer‑productivity platform, reducing time‑to‑insight by 30% for 500+ engineers through opinionated workflows and clear visualizations.
Designed type‑safe REST APIs with caching and pagination; improved P95 latency by 42% and cut error rate by 35% under production load.
Built metrics and tracing pipelines on PostgreSQL + Redis processing 2M+ events per month, enabling root‑cause analysis and feature‑level impact reporting.
Optimized frontend performance with code splitting, memoization, and bundle hygiene; reduced JS payload by ~25% and First Contentful Paint by ~18%.
Implemented CI/CD with Docker and GitHub Actions, adding smoke tests and health checks; decreased deployment rollbacks by 80% and improved MTTR.

Junior Machine Learning Engineer

Universidad Nacional de Colombia

01/2022 - 01/2023

Bogotá, Colombia

Developed quantitative trading algorithms using Python, LSTM networks, and ARIMA models for a $50K+ fund, achieving 15% improvement through automated backtesting and real-time analysis
Built NLP pipelines with fine-tuned LLMs (GPT/BERT) for financial sentiment analysis and automated report processing, reducing manual research time by 60% and achieving 92% accuracy
Implemented MLOps infrastructure using Docker, Terraform, and AWS to deploy machine learning models for real-time financial data processing and trading signal generation
Created risk management systems combining Monte Carlo simulations, VaR calculations, and feature engineering pipelines with technical indicators to optimize portfolio allocation

Let's build something amazing together

I'm always interested in new opportunities and challenging projects. Whether you need frontend expertise, AI integration, or data visualization, let's talk.

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