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AI Engineer with less than a year in Machine Learning & Deep Learning
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
A skilled professional with strong proficiency in Python and hands-on experience across supervised and unsupervised learning techniques. Adept at designing, training, and fine-tuning deep learning models like CNNs, RNNs/LSTMs, and Transformer-based architectures. Proficient in building computer vision pipelines using OpenCV for image preprocessing, object detection, and feature extraction. Experienced in leveraging modern frameworks including PyTorch, TensorFlow, Keras, scikit-learn, and Hugging Face Transformers, with skills in deploying models for production-ready inference. Demonstrated abilities in predictive analysis, data preprocessing, and model development through projects in customer churn and fraud detection.
Swarna Bharathi Institute of Science and Technology
B.Tech · Computer Science Engineering
August 1, 2021 – June 30, 2025
Vignan Junior College
Intermediate Education
June 1, 2019 – May 31, 2021
Z.P. High School
SSC Secondary Education
N/A – May 31, 2019
The Skill Union
Data Scientist Intern
October 1, 2025 – March 1, 2026
India
Customer Churn Prediction Using Machine Learning
June 1, 2026 – Present
• Built an AI-powered customer retention prediction system using telecom data. • Developed a reproducible data ingestion and preprocessing pipeline, handling missing values, outliers, and feature scaling. • Performed feature engineering and selection to improve model performance. • Applied data balancing techniques such as SMOTE to handle class imbalance and enhance prediction reliability. • Trained multiple machine learning models and selected the best performer based on ROC and AUC metrics. • Implemented a continuous inference loop to monitor predictions and improve model performance over time. • Deployed the solution on Render with basic CI for retraining and inference.
Credit Card Fraud Detection Using Machine Learning
June 1, 2026 – Present
• Built an end-to-end Machine Learning pipeline including data ingestion, preprocessing, feature engineering, and model training. • Implemented supervised learning models to accurately identify fraudulent transactions while addressing class imbalance. • Performed model evaluation using precision, recall, F1-score, and ROC-AUC. • Optimized model performance through hyperparameter tuning and validation techniques. • Deployed the trained model using a scalable prediction workflow for real-time or batch inference.
Programming with Python
Infosys Springboard
June 1, 2026 – Present
Machine Learning
Coursera
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects (Customer Churn Prediction, Credit Card Fraud Detection) demonstrate an interest in practical, business-oriented AI applications, which aligns well with many industry roles. The breadth of skills listed (ML, DL, NLP, Computer Vision) and the use of various frameworks suggest adaptability and a willingness to learn diverse technologies. The internship experience further indicates an ability to collaborate in a team environment. However, the experience level is entry-level, which might require more mentorship in a senior role.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex problems independently and as part of a team (as noted in the internship). The focus on end-to-end solutions and continuous improvement (inference loops, CI) suggests a practical, results-oriented approach. However, without direct assessment data for soft skills, this remains an inference.