
I’m a Front-End Developer and Product Enthusiast currently working as an Executive Trainee at TVS Motor Company, where I contribute to digital transformation by turning business requirements into scalable digital solutions.
I work at the intersection of business, data, and technology, collaborating with stakeholders, designers, and developers to build dashboards and enterprise applications that streamline workflows and improve efficiency. From requirement analysis and workflow design to UAT validation and release, I’m actively involved throughout the product lifecycle.
I specialize in React, Node.js, and Tailwind CSS, with hands-on experience building data-driven dashboards and integrating tools like Power BI. Alongside development, I have experience in SAP operations (user access, role management, data maintenance), which gives me a strong understanding of enterprise systems.
I’m passionate about building user-centric, data-driven products and continuously exploring ways to create intuitive, scalable, and impactful digital experiences.
Turning ideas into impactful digital solutions through technology, analytics, and innovation.
Building responsive and modern web applications using React, Next.js, and Tailwind CSS with a strong focus on performance and user experience.
Creating interactive dashboards and business intelligence solutions using Power BI to transform data into meaningful insights.
Supporting SAP operations including user access management, role creation, master data maintenance, and process optimization.
Collaborating with business teams to analyze requirements, conduct UAT, and deliver scalable digital transformation solutions.
From academic achievements to professional experience
Driving digital transformation initiatives by working on SAP operations, data analysis using Excel, and UI/UX design with Adobe XD to convert business processes into efficient digital solutions.
Explore my projects showcasing real-world applications.

Year
May - 2026

Machine Learning & MERN Stack
ML-powered web application for Parkinson’s disease prediction with real-time analysis.
Year
Mar - 2025

Year
Feb - 2025

Machine Learning & NLP
Analyzed breach datasets and achieved 90.48% prediction accuracy using NLP techniques.
Year
Nov - 2024
Industry Certifications & Credentials
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