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Software Engineer with 2+ years in Python, Machine Learning & NLP
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Computer Science graduate (CGPA: 8.44) with hands-on experience in Python, SQL, machine learning, and NLP-driven application development. Built supervised learning models (decision trees, logistic regression, random forest) and an AI-powered HR chatbot. Adept at data analysis, model evaluation, and translating business requirements into software solutions.
Sathyabama Institute of Science and Technology
B.E. in Computer Science · Computer Science
May 1, 2021 – May 1, 2025
AI-Powered HR Management System with Chatbot
January 1, 2024 – May 1, 2025
Designed an HR management system integrated with an NLP/ML chatbot for 24/7 automated employee query resolution, intent classification, and personalised response delivery. Collected interaction data to generate behavioural insights supporting data-driven HR decision-making; engineered conversation flows to handle complex multi-turn inquiries.
Credit Risk Classification – South German Credit Dataset
January 1, 2024 – December 31, 2024
Built an ensemble credit-worthiness prediction pipeline; applied feature engineering and cross-validation, comparing accuracy, precision, recall, and F1-score across three classifiers.
Abalone Age Prediction – Decision Tree Regression
January 1, 2023 – December 31, 2023
Automated abalone age estimation from physical measurements using decision tree regression; performed EDA, handled missing values, and tuned hyperparameters to optimise accuracy.
SQL and Relational Databases 101
IBM / Cognitive Class
June 1, 2026 – Present
Machine Learning Certificate of Training - Supervised Learning (Classification & Regression)
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects are primarily academic and focused on Machine Learning and NLP, which aligns with a Software Engineer role with an ML specialization. The diversity of projects (credit risk, age prediction, HR chatbot) shows a breadth of application for ML skills. However, the lack of professional experience or open-source contributions limits the assessment of cultural fit beyond technical alignment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to translate business requirements into software solutions and a focus on data-driven decision-making. The academic projects suggest a structured approach to problem-solving and an understanding of project lifecycles, albeit within an academic context. There is no direct evidence of collaboration or stress handling from the provided data.