Open to opportunities

Ashwin Upadhyay

@wtfashwin

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Detail-oriented Full Stack Engineer focused on user-friendly web applications.

India

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What I'm looking for

I am looking for opportunities that foster growth, innovation, and collaboration in a dynamic team environment.

I am a detail-oriented Full Stack Engineer with a passion for crafting user-friendly web applications. My expertise lies in a variety of technologies including HTML, CSS, JavaScript, Vue.js, and Express.js. I am committed to continuous learning and growth, always seeking to enhance my skills and knowledge in the ever-evolving tech landscape.

During my internship at Springer Capital, I built and integrated REST APIs using Node.js and Express, significantly improving uptime and reducing deployment time by 40%. My projects, such as the AI-Powered Fraud Detection System and BunHub, showcase my ability to develop innovative solutions that leverage modern technologies like React, Node.js, and Docker. I thrive in collaborative environments, working closely with designers and developers to deliver high-quality applications.

Experience

Work history, roles, and key accomplishments

Full-Stack Web Development Intern

Springer Capital

Jun 2024 - Present (2 years 1 month)

Built and integrated REST APIs using Node.js and Express to power frontend features. Dockerized services and deployed to scalable environments, improving uptime and reducing deployment time by 40%.

Node Docker React TypeScript Git

Education

Degrees, certifications, and relevant coursework

Trinity College of Engineering and Research, Pune (SPPU)

B.E., Information Technology

2021 - 2025

Grade: 8.53

Activities and societies: Team Lead, Smart India Hackathon; Published beginner-friendly articles on data science & machine learning for peers; HackerRank: Gold Badge in Python & SQL; GFG Rating: 1740+; Rank #1 in college; Top 50 SQL Coder; LeetCode Rating: 1644+, 150-day streak

Led a 5-member team to clear multiple rounds of the Smart India Hackathon. Published beginner-friendly articles on data science and machine learning for peers.