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I am a full stack web developer focused on building fast, accessible, and maintainable software using React, Next.js, Node.js, and Spring Boot. I recently completed a virtual internship at Labmentix where I helped deliver responsive web applications and scalable backend services. I also developed WeatherNow—a feature-rich forecasting app using React, Vite, and the Open-Meteo API that handles 3,000–5,000 daily users with high performance. I’m passionate about clean code, performance optimization, and creating intelligent digital solutions that make a real impact.…I am a full stack web developer focused on building fast, accessible, and maintainable software using React, Next.js, Node.js, and Spring Boot. I recently completed a virtual internship at Labmentix where I helped deliver responsive web applications and scalable backend services. I also developed WeatherNow—a feature-rich forecasting app using React, Vite, and the Open-Meteo API that handles 3,000–5,000 daily users with high performance. I’m passionate about clean code, performance optimization, and creating intelligent digital solutions that make a real impact.

Mayur Kamble

Front-End Developer, Back-End Developer, Full Stack Developer, +2

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I am a full stack web developer focused on building fast, accessible, and maintainable software using React, Next.js, Node.js, and Spring Boot. I recently completed a virtual internship at Labmentix where I helped deliver responsive web applications and scalable backend services. I also developed WeatherNow—a feature-rich forecasting app using React, Vite, and the Open-Meteo API that handles 3,000–5,000 daily users with high performance. I’m passionate about clean code, performance optimization, and creating intelligent digital solutions that make a real impact.…I am a full stack web developer focused on building fast, accessible, and maintainable software using React, Next.js, Node.js, and Spring Boot. I recently completed a virtual internship at Labmentix where I helped deliver responsive web applications and scalable backend services. I also developed WeatherNow—a feature-rich forecasting app using React, Vite, and the Open-Meteo API that handles 3,000–5,000 daily users with high performance. I’m passionate about clean code, performance optimization, and creating intelligent digital solutions that make a real impact.

Available to hire

I am a full stack web developer focused on building fast, accessible, and maintainable software using React, Next.js, Node.js, and Spring Boot. I recently completed a virtual internship at Labmentix where I helped deliver responsive web applications and scalable backend services.

I also developed WeatherNow—a feature-rich forecasting app using React, Vite, and the Open-Meteo API that handles 3,000–5,000 daily users with high performance. I’m passionate about clean code, performance optimization, and creating intelligent digital solutions that make a real impact.

Skills

Experience Level

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Beginner

Beginner

Beginner

Work Experience

Full Stack Developer Intern at Labmentix

August 1, 2025 - October 17, 2025

Developed and maintained responsive web applications using Next.js and React, improving user experience and site performance by 30%. Implemented RESTful APIs and integrated backend services using Node.js and Express, reducing data retrieval time by 25%. Optimized database queries and schema design in MongoDB, enhancing data processing speed by 40%. Contributed to automated testing and CI/CD pipeline setups using GitHub Actions, increasing deployment efficiency and reducing bugs by 20%. Participated in code reviews and agile SCRUM meetings, improving code quality and team collaboration.

Generative AI Intern at IT NetworkZ

February 1, 2025 - October 17, 2025

Developed and fine-tuned generative AI models using Python and ML frameworks (TensorFlow, PyTorch), boosting text generation quality and project delivery speed by 25%. Assisted in implementing Retrieval-Augmented Generation (RAG) pipelines, improving large language model response accuracy by 20%. Collaborated on preprocessing datasets and optimizing model training, reducing data processing time by 30%. Contributed to deployment and monitoring of AI models in production, ensuring reliability and reducing downtime by 15%. Documented workflows and key findings to streamline knowledge sharing and model reproducibility.