Fullstack Developer

Hi, I'mArturo Aliaga

I create innovative digital experiences with cutting-edge technologies

Specializing in building exceptional digital experiences that combine beautiful design with powerful functionality. From AI-powered applications to real-time systems, I bring ideas to life with code.

Profile

Tech Universe

An immersive visualization of my technical skills and expertise

Tech Universe
Solar Systems (click to explore):
Frontend Galaxy
Backend Nebula
Database Cluster
DevOps Constellation
AI Universe
Language Sector
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Featured Projects

Explore my most significant work and technical achievements

Survey Management System

Survey Management System

SurveySaaS is a multichannel survey platform that covers the entire cycle — creation, distribution, collection and analysis — with native support for email, WhatsApp, web/PWA forms, OCR of printed forms and audio/voice surveys. The backend is developed in Python with FastAPI and PostgreSQL, while the frontend uses React 18 with offline-first PWA. Includes a Flutter mobile app for offline responses and AI processing using GPT-4o for automatic analysis.

Key Features

Multichannel distribution (email, WhatsApp, web, mobile)
Offline-first PWA for responses without connection
OCR for printed form processing
Audio transcription with Whisper AI
Python
FastAPI
React 18
Flutter
PostgreSQL
GPT-4o

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Professional Journey

My career path, education, and professional achievements

Freelancer – Data Analyst & Fullstack Developer

2022 – Present

Self-employed

Designed and implemented tailored software solutions, combining backend technologies such as FastAPI and Node.js with frontend frameworks like React and Next.js. Responsibilities included building secure authentication systems, developing interactive dashboards, performing data analysis using Pandas, and creating advanced data visualizations with Plotly and Power BI. Applications were deployed using Docker and hosted on AWS infrastructure.

FastAPI
Node.js
React
Next.js
Pandas
Plotly
Power BI
Docker
AWS

Fullstack & Machine Learning

January 2025 – April 2025

Uplabs AI

Engineered end-to-end machine learning pipelines utilizing scikit-learn, XGBoost, and Pandas, focused on supervised learning tasks such as classification and regression. Integrated ML models into production-grade web systems using FastAPI and Next.js, while developing interactive user interfaces for real-time data analysis. Worked extensively with structured datasets, feature selection, and hyperparameter tuning. Deployment and infrastructure management conducted via AWS services.

scikit-learn
XGBoost
Pandas
FastAPI
Next.js
Machine Learning
AWS
Feature Selection

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