Marko Ivanovic
Software Developer at Perplexity
Banja Luka, Republika Srpska, Bosnia and Herzegovina
Summary
👤
Senior🎓
Top SchoolMarko Ivanovic is a pragmatic software developer with 11 years of hands-on experience building products from low-level C networking stacks to full-stack web and conversational AI systems. He has shipped features at scale for Yandex.Browser, rescued and commercialized a C-based video streaming SaaS, and moved startups from idea to MVP using React/Node and mobile tooling. Marko contributes to open-source spectral analysis tools—implementing SMACC and USGS library support—showing uncommon expertise in hyperspectral data processing alongside traditional engineering. Comfortable across C, C++, Python, JavaScript and embedded/async systems, he thrives on debugging complex multi-platform issues and optimizing for performance and resource constraints. Based in Banja Luka, he pairs a strong academic background with a track record of turning messy legacy code into reliable, revenue-generating products.
11 years of coding experience
8 years of employment as a software developer
Bachelor's degree, Computer and Information Sciences, 9.17/10.00, Bachelor's degree, Computer and Information Sciences, 9.17/10.00 at University of Banja Luka, Faculty of Electrical EngineeringMaster's degree, Computer Science, Master's degree, Computer Science at State University of Nizhni Novgorod named after N.I. Lobachevsky (UNN)
Russian, Serbian, English
Github Skills (10)
algorithms10
machine-learning10
python10
image-processing10
anomaly-detection9
visualization8
numpy8
visualizations8
scikit7
scikit-learn7
Programming languages (8)
Github contributions (5)
spectralpython/spectral
Jun 2020 - Jul 2020
Python module for hyperspectral image processing
Role in this project:
Data Scientist
Data ScientistContributions:6 commits, 6 PRs, 41 comments in 1 month
Contributions summary:Marko significantly contributed to the `spectral` repository, which focuses on hyperspectral image processing. Their work includes the implementation of the SMACC (Sequential Maximum Angle Convex Cone) algorithm for endmember extraction and the addition of continuum removal algorithms, demonstrating a focus on spectral analysis techniques. Furthermore, the user added support for the USGS spectral library, enhancing the project's data handling capabilities. This suggests a strong understanding of and contributions to the core functionality related to hyperspectral image processing and data analysis.
pythonhyperspectral-imageanomaly-detectionhyperspectralhyperspectral-image-classification
kormang/MuzickaGroznica
Mar 2015 - Aug 2017
Contributions:6 PRs, 54 pushes, 1 branch in 2 years 6 months
java
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