about
Aspiring Machine Learning Engineer with a passion for building intelligent systems that solve real-world problems.
background
I’m a Machine Learning and AI enthusiast currently pursuing a B.Tech in Computer Science, with experience building intelligent systems that blend practical engineering with advanced research. My work spans from model distillation and neural network visualization to NLP tasks like sarcasm detection, with a strong emphasis on performance, interpretability, and usability.
Alongside AI, I have a solid foundation in full-stack web development, having built responsive, production-grade applications using React, TypeScript, Flask, and Next.js. I actively contribute to open-source projects and stay engaged in coding competitions, constantly refining my skills across both software development and machine learning domains. Currently, I'm exploring in-depth deep learning and started with DevOps.
Education
- 2023-2027
B Tech in Computer Science & Engineering (AI & ML)
Lovely Professional University, Punjab, India
- 2022-2023
Senior Secondary (CBSE)
ARB International School, Tamil Nadu, India
Experience
- Sep 2025 - Present
Backend Developer (Intern)
Maximizze Media • Gurugram, Haryana, India (Remote)
- Jul 2025 - Present
AI Agents Developer (Intern)
Kartavya Technology • Bengaluru, Karnataka, India (Remote)
- Sep 2023 - Feb 2025
Junior ML Team Member
Google Developer Student Club - LPU • Phagwara, Punjab, India
languages
achievements
Hackathons & Competitions
National-level hackathon finalist
Round 1 Qualifier (out of 20K participants)
Consistent performance across university competitions
Competitive Programming
Contest Performance
Outstanding Div. 2 performance
Division 2 contest performance
Academic & Open Source
Exceptional entrance exam performance
Open Climate Fix, Google DeepMind OSS, Gemini research
philosophy
I believe that the most effective AI systems combine solid mathematical foundations with practical engineering. My approach focuses on:
- Research-driven implementation — Staying current with ML research but focusing on what actually works in production
- Data-Centric Thinking — Emphasizing data quality and understanding over algorithm complexity
- Iterative Development — Starting simple and iteratively improving based on real-world feedback
- Explainable AI — Building systems that provide insights into their decisions, not just predictions