About

I am Mohanbabu KR, a recent graduate with an M.Sc. in Applied Data Science from S.R.M. Institute of Science & Technology, Chennai, class of 2026. During my studies, I completed internships at Upturn Technologies and Infotact Solutions, where I developed LLM- and NLP-based tools for quality engineering and built predictive models for business intelligence. My projects include an Intelligent Quality Engineering Assistant that reduced manual QA effort by 60% and a phishing detection system with 94% accuracy. I am proficient in Python, SQL, JavaScript, HTML/CSS, Scikit-learn, TensorFlow, FastAPI, and cloud tools like Docker. I am now seeking my first full-time role as an ML Engineer, where I can apply my skills in LLMs, NLP, and backend API development to production AI systems.

Experience

1. Data Science Intern

   Upturn Technologies

   Dec 2025 – Present · Chennai, Tamil Nadu

   Develop LLM- and NLP-based tools in Python for requirement analysis and automated test generation in quality engineering workflows. Build machine learning solutions supporting test automation workflows.

2. Data Analyst Intern

   Infotact Solutions

   June 2025 – Present · Bengaluru, Karnataka

   Built predictive models and performed data analysis to support business intelligence initiatives. Automated ETL pipelines and data preprocessing workflows using Python and SQL.

Education

1. SRM Institute of Science and Technology, Chennai

   M.Sc. Applied Data Science

   2024 – 2026

2. Bharath University, Chennai

   B.Sc. Mathematics and Statistics

   2020 – 2023

Projects

• Intelligent Quality Engineering Assistant (IQEA)

  • Python
  • FastAPI
  • LLMs
  • Sentence Transformers

  Architected an LLM-based system automating requirement analysis, risk identification, and test-case generation, reducing manual QA effort by 60%. Designed a modular FastAPI backend for scalable requirement ingestion and real-time analysis. Implemented embedding-based semantic similarity with Sentence Transformers to evaluate requirement coverage across 1000+ test scenarios with 92% accuracy. Built structured prompt templates for automated test-case generation, achieving 85% coverage on critical paths. Developed a risk assessment engine using ML-based severity scoring to prioritize high-impact requirements.

• URL-Based Phishing Detection System

  • Python
  • Scikit-learn
  • Pandas
  • NumPy

  Built an ML classifier detecting phishing websites from URL lexical features, achieving 94% accuracy on 10K+ URLs. Engineered 25+ URL features (length, special-character density, domain structure, suspicious tokens) using regex. Trained and evaluated Random Forest, XGBoost, and SVM with 5-fold cross-validation; built end-to-end pipeline with preprocessing, SelectKBest feature selection, and validation.

• Student Behavior Analysis System

  • Python
  • Pandas
  • NumPy
  • Scikit-learn

  Analyzed 5000+ student activity records for the StayManager platform; EDA surfaced 12 behavioral trends tied to performance and retention. Applied K-Means clustering and classification to build student risk profiles, with a reported 18% reduction in dropout rates.

Achievements

• Intelligent Quality Engineering Assistant (IQEA)

  2025

  Architected an LLM-based system automating requirement analysis, risk identification, and test-case generation, reducing manual QA effort by 60%

• URL-Based Phishing Detection System

  2025

  Built an ML classifier detecting phishing websites from URL lexical features, achieving 94% accuracy on 10K+ URLs

• Student Behavior Analysis System

  2025

  Analyzed 5000+ student activity records for the StayManager platform; EDA surfaced 12 behavioral trends tied to performance and retention