David Lopera
R&D Manager at GreenSQA
Cali, Valle del Cauca, Colombia
Summary
👤
Senior🎓
Top SchoolDavid Lopera is an R&D Manager and seasoned software engineer based in Cali, Colombia, with two decades of hands-on experience driving innovation in automation, analytics, DevOps, and machine learning applied to software quality. He has progressed from full-stack and mobile development to technical leadership roles, co-leading R&D teams at GreenSQA and shaping backend systems as a senior engineer at Makrwatch. Equally comfortable in the .NET ecosystem and cloud platforms (Azure, Firebase), he has practical ML experience in computer vision using Keras/TensorFlow and has contributed CV/DL code for PyImageSearch crash-course projects. Known for blending applied research with production delivery, he routinely converts experimental models and DevOps practices into tangible quality improvements. His background includes ERP and enterprise systems work, giving him a rare mix of product-focused engineering and research-driven innovation. Colleagues describe him as a pragmatic technical leader who mentors teams while still writing and reviewing the core code that ships.
10 years of coding experience
5 years of employment as a software developer
Diplomado en IngenierĂa de Software y .Net, Computer Software Engineering, Diplomado en IngenierĂa de Software y .Net, Computer Software Engineering at Universidad ICESIIngeniero de Sistemas, Ingeniero de Sistemas at Universidad Santiago de Cali
English, Spanish
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (7)
computer-vision10
opencv10
caffe10
deep-learning10
python10
mask-rcnn9
faster-rcnn9
Programming languages (8)
Github contributions (5)
dloperab/PyImageSearch-CV-DL-CrashCourse
Aug 2018 - Nov 2020
Repository for PyImageSearch Crash Course on Computer Vision and Deep Learning
Role in this project:
ML Engineer
ML EngineerContributions:17 commits, 3 PRs, 15 pushes in 2 years 3 months
Contributions summary:David contributed code for computer vision and deep learning tasks using OpenCV and deep learning models. Their work involved face detection in both images and video streams. The contributions included implementing object detection using pre-trained models and setting up the necessary environment for running the code, along with reviewing and updating earlier code.
computer-visiondeep-learning
dloperab/100-Days-Of-ML-Code
Sep 2018 - Mar 2019
Contributions:86 commits, 82 pushes, 1 branch in 6 months
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