Alan Churichi

Electronics Engineer with a focus on software development for computer vision and robotics. Developed perception systems and software for embedded systems and full stack environments to build practical and reliable solutions. Curious and organized professional motivated to learn and improve through experimentation with new technologies.

Computer Vision Engineer at Pek Automotive (2025-07 – 2026-03)

• Developed logic for fruit ripeness detection on agricultural robots using a combination of classic image processing and deep learning models. Optimized the system for embedded hardware to achieve detection times of 100ms and accurate classification using very few color samples.
• Designed an automated OS installation pipeline for vision units that cut setup time to 30 minutes and ensured all systems run the latest software versions.
• Technologies: Python, PyTorch, OpenCV, Intel RealSense SDK, Docker, Linux, Bash

Full Stack Engineer at Inmar Media (2021-11 – 2025-07)

• Developed a web-based tool for creating and rendering animated ads, enabling users to set up campaigns 50% faster. Implemented an automated system for generating ad variations, improving efficiency and consistency across large-scale campaigns. Collaborated with product and design teams to deliver a seamless, high-performance user experience.
• Technologies: JavaScript, React, Bootstrap, Node.js, MongoDB, AWS

Full Stack Engineer at Hanson Robotics (2024-02 – 2024-11)

• Developed a web application for calibrating robot motors, making the process simple enough for users without a technical background. Connected the interface with ROS through WebSocket to synchronize the app with the robot control system in real time. GitHub project.
• Technologies: JavaScript, React, Bootstrap, ROSLib.js, Node.js, Express, MongoDB

Machine Learning Engineer at University of Buenos Aires (2022-11 – 2023-04)

• Developed a software tool for users to extract visual metrics like color, shape, and size from crop images to support data collection for yield prediction. Applied deep learning models for the automated analysis of fruit photos to detect specific features and patterns without manual input.
• Technologies: Python, PyTorch, OpenCV, Electron, React

Electronics Engineer at National Technological University (2021-04 – 2021-11)

• Designed and implemented a complete vision system for vehicle recognition and license plate detection, integrated with an automated billing platform to calculate parking fees based on detection time. The new system achieved faster processing performance compared to previous approaches. GitHub project.
• Technologies: Python, TensorFlow, OpenCV, C++, Node.js, Docker, PostgreSQL

Specialist Degree in Artificial Intelligence – University of Buenos Aires (2023)

Degree in Electronics Engineer – National Technological University (2021)