Sharang Pai is an AI-focused software engineer with 10 years of experience building production ML systems and scalable full-stack products across FinTech, HealthTech, and EdTech. Currently a Member of Technical Staff at Luma AI after co-founding and leading core model engineering at Persona AI, he specializes in model integration, low-latency inference, orchestration, and observability for real-world, user-facing applications. He brings a rare blend of research and product experience—from multilingual event detection research at CMU to founding an open-source education platform serving Indian schools. A proactive front-end contributor as well (notably improving responsive behavior in the popular tsparticles library), he pairs strong UI/UX instincts with backend data pipelines and deployment know-how. Comfortable shipping from prototype to production, he focuses on reliability, performance, and practical alignment with domain stakeholders. Based in San Francisco, he combines startup grit with academic rigor from Carnegie Mellon and a track record of translating complex AI into socially relevant products.
9 years of coding experience
11 years of employment as a software developer
High School, High School at Delhi Public School, Pune
Bachelor’s Degree Computers and Communications Engineering, Bachelor’s Degree Computers and Communications Engineering at Manipal Institute of Technology
Masters in Artificial Intelligence and Innovation Computer Science, Masters in Artificial Intelligence and Innovation Computer Science at Carnegie Mellon University
tsParticles - Easily create highly customizable JavaScript particles effects, confetti explosions and fireworks animations and use them as animated backgrounds for your website. Ready to use components available for React.js, Vue.js (2.x and 3.x), Angular, Svelte, jQuery, Preact, Inferno, Solid, Riot and Web Components.
Role in this project:
Front-end Developer
Contributions:5 commits in 1 day
Contributions summary:Sharang focused on enhancing the responsive behavior of the `tsparticles` library. They introduced a `mode` parameter to the `Responsive` type, allowing for screen width or canvas-based responsiveness. The commits demonstrate modifications to the options loading logic, enabling dynamic updates based on screen size adjustments. The user's work also included refactoring code to improve readability and resolve potential issues.
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Sharang Pai - Member Of Technical Staff at Luma AI