AI Engineer with less than a year in Deep Learning & Machine Learning
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Motivated AI & Machine Learning enthusiast with a strong foundation in deep learning and neural networks. Proficient in Python, NumPy, Pandas, Scikit-learn, and C++ with hands-on experience through academic and personal projects. Passionate about building intelligent systems and eager to apply technical skills to solve real-world problems.
Panipat Institute of Engineering & Technology
B.Tech · Computer Science (AI & ML)
August 1, 2022 – June 30, 2026
Face Landmark Detection (Machine Learning/Deep Learning)
June 1, 2026 – Present
Developed a real-time face landmark detection system using MediaPipe and OpenCV for accurate facial feature extraction. Built an emotion recognition model to classify facial expressions (Happy, Sad, Disgust) using TensorFlow and Keras. Preprocessed and managed dataset using NumPy and Pandas for efficient data handling. Trained the model on a custom dataset of 50 labeled images, implementing data preparation and feature extraction techniques. Visualized model performance and data insights using Matplotlib and Seaborn. Applied machine learning techniques using Scikit-learn for model evaluation and performance metrics. Implemented real-time webcam-based detection to identify facial landmarks and predict emotions dynamically. Designed an end-to-end pipeline including data collection, preprocessing, model training, evaluation, and deployment.
Breast Cancer Prediction system
June 1, 2026 – Present
Developed a Machine Learning model to predict breast cancer (benign vs malignant) using classification algorithms. Performed data preprocessing and exploratory data analysis (EDA) using Python libraries like Pandas, NumPy, Matplotlib, and Seaborn. Implemented and compared K-Nearest Neighbors (KNN) and Logistic Regression models for classification. Achieved accurate predictions by training models using train-test split technique and optimizing parameters. Visualized results using Seaborn heatmaps and plots to interpret model performance effectively.
40 Hours of Skilling program
AICTE IDEA LAB
June 1, 2026 – Present
Summer Internship on AI & ML
Imarticus Learning
June 1, 2026 – Present
Web Development Internship
OctaNet
June 1, 2026 – Present
AI & ML Internship
Brainwave Matrix Solution
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects and internships are well-aligned with an AI Engineer role, demonstrating a clear interest and foundational experience in the field. The projects show diversity in application (medical diagnosis, computer vision/emotion recognition). However, all experience is academic or internship-based, indicating a need for further development in a professional team environment. The breadth of skills is focused on AI/ML, which is good for the target role, but lacks exposure to broader software engineering practices or diverse industry applications.
Soft Skills & Operational Fit
The candidate's resume indicates a 'Motivated AI & Machine Learning enthusiast' with a passion for building intelligent systems. The academic projects demonstrate an ability to work through problem-solving scenarios and apply technical skills. However, without psychometric test results or interview data, it is difficult to assess specific soft skills like teamwork, stress handling, or communication clarity in a professional setting.