Saurabh Rai
Developer Relations Engineer
New Delhi, Delhi, India
Summary
🤩
Rockstar🎓
Top SchoolSaurabh Rai is a Developer Relations Engineer and full-stack AI practitioner with 6 years of experience building and scaling developer-focused products and communities. He leads Resume Matcher, an open-source AI/ML project that blends Python, NLP and vector search and has attracted 8.8K+ GitHub stars, 30K+ users and a 1K+ member community, demonstrating rare end-to-end product-to-community execution. At companies like SWIRL and Apideck he has driven DevRel strategy, technical content and SEO to reach hundreds of thousands of developers while also contributing to core engineering with TypeScript, Python, FastAPI and Next.js. His background includes enterprise software delivery at TCS and hands-on AI work (embeddings, semantic search, local LLM integrations), plus mentoring in Google Summer of Code—showing he balances scalable engineering with accessible technical storytelling. An interesting, less obvious strength is his combination of design and growth skills (from building the Vector Podcast site to organizing 3,000+ attendee events), which helps him translate technical features into compelling developer experiences.
6 years of coding experience
3 years of employment as a software developer
Bachelor's degree in Technology Computer Science and Engineering, Bachelor's degree in Technology Computer Science and Engineering at Guru Gobind Singh Indraprastha University
Github Skills (6)
jupyter-notebook10
python10
resume10
natural-language-processing9
scikit-learn6
scikit6
Programming languages (9)
Github contributions (5)
srbhr/Resume-Matcher
Apr 2020 - Oct 2022
Resume Matcher is an open source, free tool to improve your resume. It works by using AI, Reader LLMs, to compare and rank resumes with job descriptions.
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:2 releases, 58 reviews, 115 commits in 2 years 6 months
Contributions summary:Saurabh's contributions focused on adding and modifying a notebook with code related to resume matching. These changes involved implementing a naive approach to text similarity using cosine similarity with normalized word vectors. The user added and refined code for text cleaning, and also included documentation in the form of results and conclusions. The user demonstrated knowledge of data manipulation and potentially some understanding of natural language processing techniques.
pythonword-embeddingsatsjobdashboard
srbhr/Fruits_360
Oct 2019 - Oct 2022
Contributions:44 commits, 59 PRs, 66 pushes in 2 years 11 months
pythondeep-learningmachine-learningfruitstensorflow
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