AI Engineer with less than a year in Machine Learning & NLP
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Final-year B.Tech student in Artificial Intelligence and Machine Learning (CGPA 8.53/10) with demonstrated experience building and deploying end-to-end machine learning systems. Delivered a 60% RMSE reduction on a 10,000+ record regression task, achieved 20% accuracy improvement over baseline with an additional 10% gain via hyperparameter tuning, and built a real-time computer vision system running on live webcam feed. Seeking an AI Engineer role to apply deep learning, NLP, and predictive modeling expertise in a production environment.
Modern Institute of Technology and Research Center
B.Tech · Artificial Intelligence and Machine Learning
August 1, 2022 – June 30, 2026
Jain Senior Secondary School
Class XII
June 1, 2020 – May 31, 2021
American Sign Language Real-Time Detection
May 1, 2024 – June 1, 2024
Built a real-time ASL gesture recognition system that classifies hand signs from live webcam feed at interactive framerates with no server dependency. Designed end-to-end image preprocessing pipeline including resizing, normalization, and background removal to stabilize model inputs and reduce classification noise. Trained a CNN-based classifier on a labeled ASL dataset; achieved measurably higher recognition accuracy over the untuned baseline through model optimization.
Customer Churn Prediction Pipeline
April 1, 2024 – May 1, 2024
Built a supervised machine learning pipeline on telecom churn data; performed EDA and feature engineering to identify top churn predictors including contract type, tenure, and monthly charges. Evaluated Logistic Regression and ensemble classifiers; systematic hyperparameter tuning pushed final model accuracy 10% above the initial baseline. Deployed via Streamlit, delivering live churn probability scores with interpretable business insights for non-technical stakeholders.
Used Bike Price Prediction Engine
April 1, 2024 – May 1, 2024
Collected and processed 10,000+ real-world bike listings; performed missing value imputation, categorical encoding, and outlier removal to build a clean production-ready training dataset. Benchmarked Linear Regression, Random Forest, and XGBoost; selected XGBoost after it delivered a 60% reduction in RMSE compared to the baseline model. Achieved 20% accuracy improvement over naive baseline; applied systematic hyperparameter tuning for an additional 10% accuracy gain. Deployed as a live Streamlit web application for instant bike price estimation with zero-latency user-facing inference.
5-Day IDE Bootcamp 2026
Bootcamp
April 1, 2026 – Present
45-Day NLP, Deep Learning and GPT Technologies Internship
Industry Training
July 1, 2025 – Present
Programming with C and C++
Internshala
August 1, 2024 – Present
50-Day ML, Power BI and Tableau Summer Training
Industry Training
June 1, 2024 – Present
Cultural Fit Analysis
The candidate's projects are diverse within the AI/ML domain, covering regression, classification, and computer vision, which aligns well with an AI Engineer role. The use of various tools and frameworks (Scikit-learn, XGBoost, TensorFlow, Keras, Streamlit, Flask) demonstrates a breadth of technical exposure. However, all projects are academic, and there is no professional work experience, which might indicate a need for mentorship in a corporate environment. The certifications show a proactive approach to learning and skill development.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a results-oriented approach, focusing on quantifiable improvements (e.g., 60% RMSE reduction, 20% accuracy improvement). The deployment of projects as live web applications suggests an understanding of practical application and user-facing solutions. The academic nature of projects, however, means there is no direct evidence of collaboration within a professional team setting or handling operational challenges in a production environment.