AI Engineer with less than a year in Machine Learning & NLP
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AI/ML Engineer with hands-on experience in building machine learning and NLP-based systems, including a hybrid book recommendation engine integrated with external APIs. Skilled in Python, Scikit-learn, TensorFlow, and data preprocessing. Strong foundation in model development, evaluation, and deployment, with a passion for building scalable, real-world AI solutions.
Sage University Indore
Bachelor of Technology · CSE (A.I.)
August 1, 2021 – June 30, 2025
DPS Dewas
10th Board
N/A – Present
Kshipranjli Public H. S. School
12th Board
N/A – Present
Nexus Info (Virtual)
Data Analytics Intern
June 1, 2024 – July 1, 2024
India
AICTE, Delhi
Data Engineering Virtual Intern
January 1, 2024 – March 1, 2024
India
AICTE, Delhi
Artificial Intelligence and Machine Learning Virtual Intern
May 1, 2023 – July 1, 2023
India
EEG-Based Eye State Classification using DL
February 1, 2026 – Present
• Designed a temporal modeling pipeline for EEG signal classification using sliding-window sequence generation (window size = 20). • Implemented leakage-aware preprocessing with stratified train-test splitting and training-only feature scaling. • Developed and compared Single LSTM, Stacked LSTM, and CNN-LSTM architectures in TensorFlow. • Achieved 99.8 percent test accuracy using a stacked LSTM architecture with proper leakage-aware preprocessing and stratified train-test validation.
BookHive – Book Recommendation System
July 1, 2024 – February 1, 2025
• Designed a recommendation system using NLP-based filtering techniques. • Integrated real-time book data using Google Books API for enhanced user experience. • Delivered personalized book recommendations with summaries, ratings, and purchase links.
Twitter Sentiment Analysis
January 1, 2024 – Present
• Cleaned and preprocessed social media text data sourced from Kaggle. • Classified tweets into positive, negative, and neutral sentiments using NLP techniques. • Delivered insights through sentiment distribution analysis and visual reporting.
Cultural Fit Analysis
The candidate's academic background in CSE (A.I.) and a series of virtual internships in Data Analytics, Data Engineering, and AI/ML demonstrate a clear alignment with an AI Engineer role. The projects showcase a diverse application of AI/ML techniques, from signal classification to recommendation systems and sentiment analysis, indicating a broad interest and adaptability. The use of standard tools like Python, TensorFlow, and Git suggests a readiness to integrate into typical development environments. However, all experience is academic or virtual internship-based, which might limit exposure to real-world team dynamics and corporate culture.
Soft Skills & Operational Fit
The candidate's resume indicates a proactive approach to learning and applying AI/ML concepts through academic projects and virtual internships. The detailed descriptions of project methodologies (e.g., leakage-aware preprocessing, architecture comparison) suggest an analytical mindset and attention to detail. However, without direct interaction or psychometric test results, it's difficult to assess communication, teamwork, or stress handling abilities.