About

I am a B.Tech Computer Science graduate from S.R.M. Institute of Science & Technology, specializing in AI and Machine Learning. During my studies, I completed internships at Muonium AI Studios and Prodigy InfoTech, where I worked on image generation, customer segmentation, and image classification projects. My strongest skills include Python, TensorFlow, Keras, OpenCV, and data analysis tools like NumPy and Pandas. I am eager to take on my first full-time role as an ML Engineer, leveraging my academic and internship experience to contribute to impactful AI solutions.

Experience

1. AI Solutions Intern

   Muonium AI Studios

   Dec 2025 – Feb 2026

   Built and evaluated image generation and image annotation pipelines for AI-driven product prototypes, contributing directly to prototype software development. Collaborated with the AI team to handle and structure datasets for model training, strengthening end-to-end AI workflow experience from raw data to prototype. Adapted quickly to new tools and ambiguous problem statements, consistently delivering working prototypes within tight internship timelines.

2. Machine Learning Intern

   Prodigy InfoTech

   Dec 2024 – Jan 2025 · Mumbai, Maharashtra

   Built a customer segmentation model using unsupervised ML techniques to group customers by behavior, and an image classification model to categorize visual data. Applied Python-based ML pipelines (data cleaning, feature engineering, model training) to solve two distinct real-world business problems. Strengthened core analytical and debugging skills by iterating on model performance across both projects.

Education

1. SRM Institute of Science and Technology

   B.Tech, Computer Science — AI & Machine Learning Specialization

   2022 – 2026 · Grade: 8.79 / 10

2. Grace Matriculation Higher Secondary School

   Higher Secondary (PCMB)

   2021 – 2022 · Grade: 87.3%

Projects

• Image Generation using GANs

  • Python
  • TensorFlow/Keras

  Designed and trained a Generative Adversarial Network (generator + discriminator) from scratch to synthesize realistic images from a training image dataset. Tuned network architecture and training loop to stabilize adversarial training and improve output image quality, gaining hands-on depth in deep learning architectures.

• Music Recommendation System

  • Python
  • OpenCV
  • Deep Learning

  Built a real-time facial emotion recognition pipeline using OpenCV and a deep learning classifier to detect user mood from webcam input. Mapped detected emotions to a curated song recommendation engine, improving personalization and user experience over static playlists.

Certifications

• Data Analysis with Python

  IBM

• Azure AI Fundamentals

  Microsoft

• Introduction to Machine Learning

  NPTEL

• Data Mining

  NPTEL

• Software Testing

  NPTEL