Moses Olafenwa
Senior Software Engineer at Bark.com
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolMoses Olafenwa is a Senior Software Engineer with eight years of experience building production-ready software and applied AI solutions, currently driving engineering at Bark.com. A self-taught programmer and deep learning practitioner, he created ImageAI—a widely used open-source Python library with 200,000+ users—and has contributed super-resolution and model-integration work to cross-platform AI projects like DeepStack. His background spans startups and large tech firms including Microsoft and Babylon, combining hands-on model development (DenseNet, ResNet, Inception, SqueezeNet) with product-focused engineering. Moses is passionate about computer vision and practical AI innovations that deliver real-world impact, and he intentionally blends research, tooling, and developer experience. Based in the UK, he brings entrepreneurial leadership from cofounding DeepQuest AI and a track record of shipping robust ML systems with minimized external dependencies.
8 years of coding experience
15 years of employment as a software developer
AGSI
Stackoverflow
21reputation
16kreached
3answers
0questions
Github Skills (26)
pytorch10
python10
image-recognition10
keras10
deep-learning10
tensorflow10
object-detection10
computer-vision10
densenet9
resnet9
faster-rcnn8
mask-rcnn8
machine-learning8
azure-pipelines6
azure6
Programming languages (9)
Github contributions (5)
OlafenwaMoses/ImageAI
Mar 2018 - Dec 2022
A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities
Role in this project:
ML Engineer
ML EngineerContributions:15 releases, 8 reviews, 298 commits in 4 years 10 months
Contributions summary:Moses contributed code to build a computer vision library focused on image prediction capabilities. Their work involved creating and integrating deep learning models, specifically DenseNet, InceptionV3, ResNet50, and SqueezeNet architectures, utilizing TensorFlow/Keras. The user also added image preprocessing functionality and integrated model weights to enable image recognition and object detection tasks. Further contributions included adding support to the ImageAI library and example usage with different models.
pythonsqueezenetimage-predictionimage-recognitionself-contained
johnolafenwa/DeepStack
May 2020 - Jun 2022
The World's Leading Cross Platform AI Engine for Edge Devices
Role in this project:ML Engineer
ML EngineerContributions:2 reviews, 35 commits, 9 PRs in 2 years 1 month
Contributions summary:Moses primarily focused on enhancing the super-resolution capabilities within the DeepStack project, specifically by implementing and integrating a super-resolution model. They added the `superresolution4x` function and associated utilities. They also addressed feedback by refactoring and modifying the super-resolution module, removing dependencies like cv2 and scikit-image and fixing bugs. Additionally, the user updated the Dockerfiles and added testing code for the enhance endpoint.
scene-recognitiondeep-learningedge-devicesface-recognitioncomputer-vision
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