AI Engineer skilled in Machine Learning, Deep Learning & Computer Vision
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AI&ML Engineer with hands-on experience in Machine Learning, Deep Learning, Computer Vision, and Predictive Analytics. Skilled in Python, SQL, TensorFlow, Scikit-learn, Pandas, and NumPy for developing end-to-end ML pipelines including data preprocessing, feature engineering, model training, evaluation, optimization, and deployment. Experience building real-time prediction systems, CNN-based image classification models, and healthcare analytics solutions. Published researcher with strong analytical and problem-solving abilities.
AVN Institute of Engineering and Technology
B.Tech · Computer Science and Engineering (AI & ML)
January 1, 2021 – January 1, 2025
Sleep Disorder Classification using Machine Learning
March 1, 2025 – May 1, 2025
Developed an end-to-end machine learning pipeline for multi-class sleep disorder prediction using 13 healthcare features. Performed data cleaning, feature engineering, normalization, and categorical encoding to improve model performance. Trained and compared KNN, SVM, Random Forest, Decision Tree, and ANN models. Improved ANN accuracy to 92.92% using Genetic Algorithm-based hyperparameter optimization. Built and deployed a Django web application enabling real-time healthcare predictions. Evaluated models using Precision, Recall, F1-score, and Confusion Matrix analysis.
Classification of Real and Fake AI Generated Images
October 1, 2024 – November 1, 2024
Developed a CNN-based image classification model to distinguish real and AI-generated images. Designed image augmentation pipelines using rotation, normalization, flipping, and contrast enhancement. Achieved 95.5% test accuracy through iterative model optimization. Conducted performance analysis using confusion matrices and training-validation metrics. Published research findings in IJERST (ICCIASH Conference 2025).
SQL Certification
HackerRank
October 31, 2025 – Present
Programming Certification of Excellence
Innomatics Research Labs
September 30, 2025 – Present
CNN-Based Image Classification Research Publication
IJERST (ICCIASH Conference 2025)
August 31, 2025 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate initiative and a focus on practical applications in AI/ML, aligning well with a role that values innovation and problem-solving. The publication indicates a drive for knowledge sharing and contribution. The breadth of skills and project diversity suggest adaptability. However, without more information on collaborative experiences or work attitude, a full cultural fit assessment is not possible.
Soft Skills & Operational Fit
The candidate's project descriptions indicate strong problem-solving abilities and a structured approach to ML development. The publication suggests a capacity for independent research and clear communication of technical findings. However, without specific psychometric or English test results, a comprehensive assessment of soft skills and operational fit is limited.