Managing Director At Learnix , Pune at Alan Scott LearniX Private Limited
Pune District, Maharashtra, India
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Summary
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Rockstar
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Top School
Jitesh Jain is a multidisciplinary technology leader and entrepreneur with six years of experience building AI-driven products and education platforms, currently serving as Managing Director at Learnix in Pune and co-founder/CEO of EduRishi Eduventures. He holds a PhD in Quantum Physics and has applied that research mindset to practical problems—from particle accelerator experiments at CERN to deploying AI and quantum-inspired solutions for finance, industrial automation, and schooling. Jitesh blends academic rigor with hands-on engineering, contributing to open-source ML tooling such as OneFormer by improving demos, CPU inference and usability for broader audiences. His background in school leadership and curriculum design informs a unique focus on tech-enabled learning experiences and scalable educational impact. Known for turning complex scientific concepts into demonstrable prototypes, he prioritizes accessibility and performance when bringing models into real-world settings. Based in Pune, he combines deep physics expertise with entrepreneurial grit to bridge research, product and operational execution.
6 years of coding experience
3 years of employment as a software developer
Master of Science - MS, Physics, Master of Science - MS, Physics at JRN Rajasthan Vidyapeeth
Doctor of Philosophy - PhD, Quantum Physics, A Grade, Doctor of Philosophy - PhD, Quantum Physics, A Grade at University of Allahabad
Quantum Physics exploration to Gravitational waves -LIGO LAB, Quantum Physics exploration to Gravitational waves -LIGO LAB at Boston University
OneFormer: One Transformer to Rule Universal Image Segmentation, arxiv 2022 / CVPR 2023
Role in this project:
ML Engineer
Contributions:3 reviews, 32 commits, 5 PRs in 3 months
Contributions summary:Jitesh primarily contributed to the OneFormer project by adding and updating the colab demo notebooks to showcase the capabilities of the model. They integrated and updated links, added support for DiNAT, and enabled CPU inference for the MultiScaleDeformableAttention kernel. Furthermore, the user addressed minor issues, fixed speed tests, and enhanced the colab demo with added results plotting. These changes focus on making the model accessible, demonstratable, and performing efficiently on different hardware.
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