Jay Rambhia is a Senior Android Engineer based in Amsterdam with 14 years of experience building production mobile apps and shipping reliable Android systems at scale. Currently at Spotify after several senior roles at Booking.com, he combines strong ownership and collaboration skills with deep hands-on expertise in Android and full-stack development. Early work includes camera and computer-vision projects (Lenx) and notable open-source contributions to SimpleCV, where he implemented DFTs, advanced filters and SURF tracking while improving memory management. He also maintains cross-platform DevOps tooling for OpenCV installs, reflecting a practical blend of ML/vision, native performance (NDK/Renderscript) and deployment know-how. Collected experience across startups and large platforms gives him a knack for turning research-grade vision algorithms into performant mobile features. Collected training in mechanical engineering underpins a pragmatic, systems-oriented approach to solving complex engineering problems.
14 years of coding experience
8 years of employment as a software developer
B.E., Mechanical Engineering, B.E., Mechanical Engineering at Birla Institute of Technology and Science
Electrnoics, Electrnoics at Ramnivas Ruia Junior College
shell scripts to install different version of OpenCV in different distributions of Linux
Role in this project:
DevOps Engineer
Contributions:49 commits, 22 PRs, 17 pushes in 4 years 6 months
Contributions summary:Jay primarily contributed to the project by creating and modifying shell scripts to install OpenCV. They added scripts for installing different versions of OpenCV (2.4.0, 2.4.1, 2.4.2, 2.4.4, 2.4.5) on Ubuntu and Arch Linux. Additionally, the user made changes to the installation scripts, including updates to package sources and dependencies.
Contributions summary:Jay primarily focused on modifications to the SimpleCV library, particularly enhancing its image processing and computer vision capabilities. Contributions include implementing DFT (Discrete Fourier Transform) functionality, developing various image filters (Gaussian, Butterworth, and notch filters), and adding support for the SURFTracker. The user also made changes to address memory management issues within the tracking algorithms.
pytorchpythonvisiondeep-learningcomputer-vision
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