MOHAMMED SAQLAIN
AI Engineer and Distributed Systems Enthusiast

Mohammed Saqlain

AI Engineer
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About

// A brief introduction

Hey! I'm Mohammed Saqlain, a Computer Science student at PES University, Bangalore and a tech enthusiast who loves solving problems, building smart solutions, and diving into the world of AI and data. Whether it's coding, analyzing trends, or experimenting with new technologies, I thrive on turning ideas into reality. When I'm not in front of my laptop, you'll find me on the cricket field or practicing martial arts — I'm a Black Belt Dan 1!

More About Me

Skills

Programming languages: Python Java JavaScript C SQL R
Machine Learning: TensorFlow PyTorch Pandas NumPy Scikit-learn HuggingFace Keras NLTK LangChain
Cloud and Deployment: Git GitHub AWS FastAPI Docker

Experience

Research Intern

June 2025 - Present
Indian Institute of Science (IISc) (Remote)
  • Working with the machine learning team at the NMCAD lab in the department of aerospace engineering.

AI Engineer Intern

June 2025 - September 2025
ProjektAnalytics (Remote)
  • Designed and optimized APIs from scratch for a compliance platform, improving performance and reducing response latency by over 40%.
  • Developed a RAG-based chatbot for the compliance platform to answer ABPI cases and queries using sparse and dense retrievals for more accurate responses.

    • Projects

    Presently

    AI-Powered Presentation Video Generator

    Python Multimodal AI LLMs
    Details

    RetroReels

    Automated YouTube Shorts generation using AI

    Python LLMs TTS GitHub Actions Automation
    Details

    TopiQ

    Intelligent Topic Discovery Powered by LLMs and Graph Insights

    Python NLP LLMs Graph Analytics
    Details

    Publications

    Reading Between the Lines: LLM-Powered Topic Modelling and Graph-Based Insights from Research Abstracts, ICIVC 2025

    Topic Modelling BERTopic LLama2 Graph Analytics LLM

    This study presents an innovative approach to research abstract analysis by combining advanced topic modeling with large language models (LLMs) and graph-based analytics. We employ BERTopic for topic extraction, LLama2 for semantic enhancement, and graph neural networks to uncover hidden patterns and relationships within academic literature. Our methodology demonstrates significant improvements in identifying emerging research trends, cross-disciplinary connections, and knowledge evolution patterns compared to traditional approaches.

    Coming Soon

    Comparative Analysis of Traffic Accident Detection with emphasis on explainability of DL Models, ICMBDC 2024

    Deep Learning Computer Vision Explainable AI SHAP

    This paper investigates traffic accident detection using deep learning models with a focus on explainability using SHAP. The study demonstrates how explainable AI makes traffic monitoring systems more trustworthy and interpretable for real-world applications.

    Open Source Contributions

    Added Differential Diffusion to Kolors

    HuggingFace Diffusers

    Python Diffusion Models Image Generation PyTorch

    Optimized ALBERT Test Model Size

    HuggingFace Transformers

    Python Transformers Model Optimization NLP

    Improved Test Suite for Lumina Transformer

    HuggingFace Diffusers

    Python Testing Diffusion Models PyTorch

    Achievements

    Knight at Leetcode

    May 2025
    Achieved the title of Knight at Leetcode, recognizing exceptional problem-solving skills and contributions to the Leetcode community.

    First Runner-Up, Flipr Hackathon 26.1 (ML/AI)

    Apr 2025
    Secured the First Runner-Up position in the ML/AI track at Flipr Hackathon 26.1 for building an AI powered personal finance tracker.

    Black Belt Dan 1 in Karate

    2021
    Attained the rank of Black Belt Dan 1 in Karate, reflecting dedication, discipline, and expertise in martial arts.