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AI Engineer with 1+ years in Data Analysis & Machine Learning.
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Computer Engineering specializing in Data Analysis and Artificial Intelligence. Proficient in machine learning, deep learning, and predictive modeling with hands-on project experience. Passionate about applying data-driven solutions to solve complex real-world challenges.
Modern Academy For Engineering and technology
Bachelor · Computer Engineering
August 1, 2021 – June 30, 2026
National Telecommunication Institute - NTI
Big Data Trainee
September 1, 2025 – Present
India
Amit Learning
AI&Data Science Trainee
June 1, 2025 – July 1, 2025
India
Yat Learning
Web Design Trainee
September 1, 2024 – October 1, 2024
India
Waste Detection
July 1, 2025 – July 1, 2025
Developed advanced waste detection system using YOLOv8-v11 to identify 22 waste material types including cans and plastic bottles. Achieved 94% mAP50 accuracy through strategic data preprocessing, augmentation, and model optimization techniques. Trained on large-scale dataset of 24,000 images from Roboflow, demonstrating expertise in computer vision and machine learning
Land Type Classification
June 1, 2025 – June 1, 2025
Developed a deep neural network model to classify land types from Sentinel-2 satellite images using the EuroSAT dataset, achieving 94% accuracy. Enabled applications in urban planning and environmental monitoring by identifying agriculture, urban, water, desert, roads, and forest areas. Supported advancements in precision agriculture and city development through robust computer vision technology.
COVID-19 X-ray Detection
May 1, 2025 – May 1, 2025
Developed a CNN-based system using deep learning for COVID-19 detection from chest X-rays with real-time prediction and confidence visualization. Implemented multiple convolutional layers with max pooling and dropout, achieving over 95% accuracy for robust performance. Enabled potential 60% acceleration in clinical diagnosis, enhancing efficiency in medical settings.
Air Delay Prediction
April 1, 2025 – April 1, 2025
Built a neural network model using TensorFlow, Keras, and Python to predict weather-related flight delays with 99.90% accuracy. Leveraged advanced preprocessing, feature engineering, and Adam optimizer with early stopping for optimal model performance. Enabled potential $2.5M annual savings for airlines by improving scheduling and reducing passenger compensation costs.
Customer Churn Prediction
December 1, 2024 – January 1, 2025
Developed a churn prediction system using XGBoost, Random Forest, SVC, and KNN, optimized with GridSearchCV, achieving 92% accuracy. Enabled proactive customer retention strategies, potentially reducing churn by 25% with XGBoost model. Projected to increase customer lifetime value by $1.8M annually through improved retention efforts.
Deep Learning Fundamentals
NVIDIA
June 1, 2026 – Present
AI and Data Science
Amit Learning
June 1, 2026 – Present
Web Design
Yat Learning
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects show a diverse interest in applying AI to various domains (waste detection, flight delays, medical diagnosis, land classification, customer churn). This breadth of application suggests adaptability and a willingness to tackle different types of problems, which is a positive indicator for cultural fit in a dynamic environment. The internships, while short, also show an eagerness to learn and apply new technologies. However, the experience level is junior, and the target role is senior, which might indicate a gap in expectations regarding independent contribution and mentorship.
Soft Skills & Operational Fit
The candidate demonstrates problem-solving skills and teamwork through project descriptions. The academic background and internship experiences suggest a structured approach to learning and project execution. However, the resume lacks specific examples of leadership, complex problem resolution in a team setting, or handling project ambiguities, which are crucial for a senior role. The communication of project impacts is clear, but the overall professional experience is limited to internships, indicating a need for more operational exposure.