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AI ML Engineer with 1+ years in Python & Machine Learning
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Machine Learning Engineer with hands-on experience building, evaluating, and deploying end-to-end predictive models for fraud detection, regression, and customer segmentation. Proficient in Python, scikit-learn, XGBoost, and Flask with strong foundations in data preprocessing, feature engineering, and model optimization. Skilled at deploying ML-powered web applications with MySQL backends. Currently pursuing MCA at GL Bajaj Institute of Technology & Management, Greater Noida, seeking an internship to apply and grow technical skills in a professional ML/AI environment.
GL Bajaj Institute of Technology & Management, Greater Noida
Master of Computer Applications (MCA)
August 1, 2025 – June 30, 2027
Chaudhary Charan Singh University, Meerut
Bachelor of Computer Applications (BCA)
N/A – June 30, 2025
Self-Directed
ML Project Developer (Academic & Personal Projects)
January 1, 2025 – Present
India
Credit Card Fraud Detection System
January 1, 2025 – Present
Built a real-time fraud detection system using XGBoost classifier and Autoencoder neural network; achieved ROC-AUC score of 0.97 on held-out test data. Resolved severe class imbalance (fraud rate < 1%) by applying SMOTE, boosting model recall for fraudulent transactions from 61% to 86%. Engineered 15+ statistical and transaction-based features (velocity, aggregation, z-score outliers), improving model F1-Score by 18%. Deployed full-stack Flask web application for real-time prediction; integrated MySQL database for storing 10,000+ transaction records and audit logs. Evaluated model using ROC-AUC, Precision-Recall curve, and Confusion Matrix; optimized decision threshold to reduce false negatives.
View ProjectHouse Price Prediction
January 1, 2025 – Present
Developed and benchmarked regression models (Linear Regression vs XGBoost) to predict residential property prices, achieving R² of 0.89 with XGBoost. Performed end-to-end preprocessing: missing value imputation, feature scaling (StandardScaler), and one-hot encoding for 20+ features. Reduced RMSE by 22% over baseline Linear Regression by applying XGBoost with GridSearchCV hyperparameter tuning (100+ parameter combinations tested).
View ProjectCustomer Segmentation — K-Means Clustering
January 1, 2025 – Present
Segmented 5,000+ customer records into 4 distinct behavioral clusters using K-Means, enabling targeted marketing strategy recommendations. Determined optimal cluster count using Elbow Method and Silhouette Score analysis (best score: 0.68), ensuring statistically valid groupings. Conducted full EDA and produced 10+ visualizations using Matplotlib/Seaborn; derived 3 actionable business insights for customer retention.
View ProjectMachine Learning (Supervised & Unsupervised) course completions
Kaggle
January 1, 2025 – Present
ML Crash Course — completed core modules including model training, overfitting, and embeddings
January 1, 2025 – Present
Cultural Fit Analysis
The candidate shows a strong interest in the AI/ML domain through academic projects and self-directed learning. The diversity of projects (fraud detection, regression, clustering) indicates a broad interest in applying ML to different problem types. The pursuit of an MCA degree while actively developing projects aligns with a growth-oriented mindset. However, the lack of professional experience means cultural fit in a corporate setting is largely unproven.
Soft Skills & Operational Fit
The candidate's self-directed project work demonstrates initiative and a proactive approach to learning and application. The detailed project descriptions suggest good organizational skills and an understanding of project lifecycle. However, without direct work experience, the ability to collaborate in a team, handle stress, or adapt to corporate operational environments is not directly assessable.