Hello, I'm

SDE Intern @ Aptos · CSE-AIML @ PESU
Knight @ LeetCode · 3x Published

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About me

About Me

Hey folks! I'm Musadiq, a CSE-AIML graduate from PES University, Bangalore, currently interning as a Software Developer at Aptos India. I'm curious and driven, with interests spanning full-stack development, deep learning, and explainable AI.

I enjoy diving into new tech, picking up hands-on experience, and learning through real world applications from building production grade software to researching RAG systems and LLMs. Along the way, I love connecting with like minded individuals and sharing knowledge as we grow together in this ever evolving field.

I'm always on the lookout for exciting opportunities to upskill and make meaningful contributions.

  • PES University – B.Tech in Artificial Intelligence and Machine Learning (2022–2026)
    • Grade: 8.26
  • BGS PU College – Pre-University (PCMC, 2020–2022)
    • Grade: 98.5% (State 8th Rank, 591/600)
  • Vidyanidhi Public School – ICSE (2010–2020)
    • Grade: 93%

Experience

Software Developer Intern

Aptos India Pvt Ltd

Internship

Jul 2026 – Dec 2026

Bengaluru, Karnataka · On-site

Incoming SDE Intern at Aptos India, joining the Bengaluru Development Centre in July 2026. Looking forward to contributing to enterprise retail technology solutions while learning from experienced engineers.

Research Intern

CDSAML, PES University

Internship

Jan 2026 – May 2026

Bengaluru, Karnataka · On-site

Building an end-to-end Legal Judgment Prediction system for Indian Supreme Court cases using RAG and fine-tuned LLaMA 3.1 8B. Structured a dataset from 700+ cases and achieved BERTScore F1 of 0.85 on held-out evaluations.

LLMsRAGNLPFine-tuningLLaMA

Skills & Technologies

Programming Languages

PythonC++CJavaJavaScriptHTMLCSS

Databases & Tools

MongoDBMySQLGitDockerPyCharmVS CodeGitHubGoogle ColabKaggle

ML Libraries & Frameworks

TensorFlowPyTorchPandasNumPyScikit-learnNLTKOpenCVHugging Face

Web Development

Node.jsExpressReactNext.jsHTMLCSS

My Projects

  • KG RAG CrimeFilm

    Built a Knowledge Graph-enhanced RAG system for in-depth analysis of crime film narratives.

    Tech Used: Knowledge Graph, RAG, LLAMA 3.1, Neo4j , Ollama
  • Multi Modal Meme Virality Prediction using HGNNs

    Created multimodal meme virality predictor using Hypergraph Neural Networks for classification.

    Tech Used: HGNNs, CLIP, BERT
  • Spotify DBMS

    Built Spotify-clone music streaming DBMS with user/admin roles, search & authentication.

    Tech Used: MySQL, React.js , JavaScript
  • Calendar (Microservice)

    Built Calendar microservice for teams to manage and share events efficiently.

    Tech Used: Docker, Microservice
  • ReTweetify: Explaining & Rewriting Turkish Hate Speech

    Built BERT-based pipeline to classify, explain, and rewrite Turkish hate speech tweets.

    Tech Used: Python, Pandas, Transformers, SHAP
  • Sorting Visualizer

    Built web-based tool for real-time visualization of various sorting algorithms.

    Tech Used: JavaScript

Stats & Activity

LeetCode Stats
GitHub Stats

Research Papers

Comparative Analysis of Traffic Accident Detection with Emphasis on Explainability of Dl Models

Computer Vision

Computer VisionMachine LearningImage ClassificationDeep LearningExplainable AI

Utilized traditional ML and deep learning models including CNN, LSTM, and YOLOv8 to classify traffic accident images, with SHAP-based interpretability for enhanced decision transparency.

PublishedView Paper

Beyond Loyalty and Betrayal: A Knowledge Graph-Enhanced RAG System for Crime Film Insights and Analysis

Natural Language Processing

Knowledge GraphsRetrieval-Augmented GenerationFilm AnalysisNLP

Developed a Knowledge Graph and RAG system for analyzing crime films, enabling nuanced question answering and thematic exploration beyond factual retrieval.

PublishedView Paper

HyperGNNs for Multi-Modal Classification and Severity Analysis of Neurodegenerative Disorders

Graph Neural Networks

Graph Neural NetworksMulti-Modal LearningHealthcare AIDeep Learning

Designed a Hypergraph Neural Network (HyperGNN) framework for multi-modal classification and severity prediction of neurodegenerative disorders, integrating heterogeneous clinical data to improve diagnostic accuracy and interpretability.

PublishedComing Soon

Let's Connect

Always happy to connect, collaborate or chat about ML and SDE. Feel free to reach out!

kmusadiqpasha@gmail.com
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