Neeraj Anand is a research-focused deep learning engineer with six years of experience working across NLP, computer vision, multimodal learning, and model efficiency, currently a Research Associate at Adobe and a Kaggle Competition Expert. His background blends internships and applied research roles at Microsoft, Amazon, and several AI labs, where he has moved ideas from prototype to production-ready experimentation. He contributes to open-source projects—having improved UI and API integration for the widely used CC Search frontend—demonstrating full-stack sensibilities alongside research rigor. Trained at IIT (ISM) Dhanbad in Mathematics and Computing, Neeraj combines strong theoretical foundations with practical engineering, often optimizing models for efficiency and real-world deployment. Colleagues describe him as a code‑addict who enjoys debugging complexity and turning research insights into usable systems.
6 years of coding experience
ug, Computer Science, ug, Computer Science at Indian Institute of Technology Jodhpur
[PROJECT TRANSFERRED] CC Search is a search tool for CC-licensed and public domain content across the internet.
Role in this project:
Front-end Developer
Contributions:9 reviews, 28 commits, 7 PRs in 1 month
Contributions summary:Neeraj primarily focused on updating and implementing UI components within the Vue.js frontend, specifically modifying the `FilterBlock.vue`, `FooterSection.vue`, `SearchHelpPage.vue`, and `SourcePage.vue` files. The contributions include adding a banner component, enhancing the header section, and incorporating API integration. They also addressed mobile view improvements.
Contributions:92 commits, 2 PRs, 95 pushes in 2 years 2 months
reactjsjavascriptreactanand
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