Amrutha Dilip Kumar
Summary
Results-driven AI/ML Intern with experience in Python and machine learning. Optimized radar signal classification models and enhanced automotive dataset preprocessing through innovative feature engineering and real-time AI applications. Collaborated within teams to deliver impactful solutions in fast-paced environments.
Overview
1
1
year of professional experience
1
1
Certification
Work History
AI/ML Intern - Radar Systems
Magna
Bangalore
01.2025 - 01.2026
- Assisted in developing and evaluating ML models for signal classification, contributing to improved classification accuracy
- Worked with PyTorch/TensorFlow training workflows to implement and streamline model training processes
- Supported model optimization and real-time AI tasks
- Preprocessed and analyzed automotive radar datasets in Python to enhance data quality for modeling
Education
M.Tech - Computer Science (AI & ML Specialization)
Vellore Institute of Technology
Vellore07-2026
Bachelor's - Computer Science
SCMS School of Engineering And Technology
Kerala06-2024
Skills
- Machine Learning
- Deep Learning
- Data Preprocessing
- Feature Engineering
- Model Training
- Model Evaluation
- Classification
- Data Augmentation
- Deployment
- Python
- Pandas
- NumPy
- Scikit-learn
- PyTorch
- TensorFlow
- C
- Embedded Systems
- OpenEmbedded SystemsV
- FAISS
- Git
- VS Code
- PyCharm
- Jupyter Notebook
- Jira
Certification
- Udemy: Python Programming Masterclass
- IBM (Coursera): Machine Learning with Python
- Coursera: Generative AI with Large Language Models
Languages
- Hindi
- Malayalam
- English
- Hindi
- Malayalam
Publications
Alzheimer Classification using EfficientNetB0 + CBAM/SE, IEEE Xplore, CVMI 2025, 10.1109/CVMI66673.2025.11337538
Projects
- Disease Classification with RAG Explainability, 05/31/26, Built CNN-based GI disease classification system using the Kvasir dataset, Developed RAG pipeline using FAISS retrieval and LLM-generated explanations, Implemented NLP-based medical query workflows and scalable FastAPI inference, Evaluated retrieval relevance and model performance across medical queries
- Alzheimer Classification using EfficientNetB0 + CBAM/SE, 05/31/25, Developed attention-based MRI classification model using EfficientNetB0 with CBAM/SE, Performed preprocessing, augmentation, feature extraction, and hyperparameter tuning, Evaluated model performance using accuracy, precision, recall, and F1-score
- Anomaly Detection using ResNet50, 05/31/24, Developed ResNet50-based system for fire/fight/normal classification (17K+ images), Optimized preprocessing pipeline for large-scale data and real-time inference, Integrated real-time alert system using Twilio API
Technical Summary
AI/ML enthusiast with hands-on experience in Python, Machine Learning, Deep Learning, and data preprocessing. Familiar with TensorFlow and PyTorch for AI model development and evaluation.
Internship
Magna, Bangalore, Karnataka, AI/ML Intern - Radar Systems, 08/01/25, 05/31/26, Preprocessed and analyzed automotive radar datasets using Python, Assisted in ML model development and evaluation for signal classification, Worked with PyTorch/TensorFlow training workflows, Supported model optimization and real-time AI tasks
Languages
English
Proficient (C2)
C2
Hindi
Proficient (C2)
C2
Timeline
AI/ML Intern - Radar Systems
Magna
01.2025 - 01.2026
M.Tech - Computer Science (AI & ML Specialization)
Vellore Institute of Technology
Bachelor's - Computer Science
SCMS School of Engineering And Technology