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AI Engineer with 1+ years 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
MCA graduate with hands-on experience in Machine Learning, Deep Learning, and Data Analysis. Skilled in Python, SQL, TensorFlow, and model development, with experience building end-to-end AI solutions involving data preprocessing, feature engineering, model training, and evaluation. Developed projects in healthcare audio classification and computer vision. Strong analytical, problem-solving, and communication skills gained through both technical projects and teaching experience. Seeking opportunities in AI/ML Engineering, Data Science, and Analytics.
Mangalore University, Konaje
Master of Computer Applications (MCA)
August 1, 2024 – June 30, 2024
Canara College, Mangalore
Bachelor of Science (B.Sc)
August 1, 2022 – June 30, 2022
Yenepoya Institute of Arts, Science, Commerce & Management
Lecturer - Computer Applications
August 1, 2024 – Present
India
Cough Sound Classification (ML & DL)
June 23, 2026 – Present
Framed a multiclass classification problem to identify six respiratory conditions (Asthma, Croup, Pneumonia, LRTI, URTI, Normal) from cough audio recordings. Performed audio preprocessing and engineered time-frequency features using MFCC and Empirical Mode Decomposition (EMD). Implemented transfer learning using MobileNetV2 to improve classification performance on limited medical data. Evaluated model performance using accuracy and confusion matrix, achieving 70% accuracy on real-world, noisy audio data. Project demonstrates end-to-end ML pipeline design, feature extraction, and model evaluation in a a healthcare context.
Coconut Leaf Disease Identification
June 23, 2026 – Present
Developed a CNN-based image classification model to detect five coconut leaf diseases using TensorFlow and Python. Performed data preprocessing, image resizing, augmentation, and class balancing to improve model generalization. Achieved 97% accuracy on validation data using model optimization. Evaluated model performance using Accuracy and Confusion Matrix.
Cultural Fit Analysis
The candidate's academic projects in healthcare audio classification and computer vision demonstrate a breadth of application areas within AI/ML. The lecturer role indicates an ability to simplify complex topics, which can be valuable for team collaboration and knowledge sharing. The projects align well with an AI Engineer role, showcasing practical application of ML/DL techniques.
Soft Skills & Operational Fit
The candidate's experience as a lecturer suggests strong communication, problem-solving, and logical thinking skills, which are beneficial for explaining complex technical concepts and collaborating within a team. The academic projects demonstrate an ability to work independently on end-to-end ML pipelines.