Akash Verma is a self-taught deep learning engineer and co-founder with six years of hands-on experience building AI products and open-source contributions. Based in Mumbai, he has moved from GSoC projects—adding Whisper-based speech-to-text to Kolibri and building hvpy for solar data visualization—to commercial roles creating AI agents and a fashion-focused Super Fashion Intelligence at Wardrov. He blends front-end polish and back-end numerical work, having fixed UI/RTL issues for Kolibri and implemented NumPy-based time series functionality for SunPy. Known for training large neural networks and an uncompromising drive for excellence, he favors practical experimentation and rapid iteration. His background includes accessible-impact projects (offline education, solar physics) and assistive-technology work improving voice recognition for users with motor injuries. This combination of open-source pedigree and product-focused AI engineering gives him both research curiosity and startup execution chops.
6 years of coding experience
1 year of employment as a software developer
Bachelor of Technology - BTech, Computer Science, Bachelor of Technology - BTech, Computer Science at Shri Shankaracharya Institute of Professional Management and Technology
Contributions:33 reviews, 20 commits, 5 PRs in 2 months
Contributions summary:Akash contributed to the `sunpy/sunpy` repository by implementing and testing core functionality related to time series data. They focused on creating and validating the use of NumPy arrays for time series generation within the GenericTimeSeries class. The contributions also include merging branches and modifying code related to solar rotation calculations and tests.
Kolibri Learning Platform: the offline app for universal education
Role in this project:
Front-end Developer
Contributions:6 reviews, 6 commits, 6 PRs in 14 days
Contributions summary:Akash primarily contributed to the front-end components of the Kolibri Learning Platform. They fixed UI issues, such as button states and text alignment for right-to-left languages. The user also made improvements to the learning platform's user interface, including the addition of a merge. Finally, the user addressed linting errors.
learning-platformoerofflineoffline-firstkolibri
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