Akash Antony
IT Data Engineer @Qualcomm
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WORK HISTORY
IT Data Engineer @Qualcomm
Munich, DE
Project – Automatic Defect Classificationo Developed, and deployed a Deep multi-class Convolutional Neural Network model with a fine-tunedpretrained backbone and custom-defined layers for defect classification.o Trained the model in a multi-GPU environment, achieving 93%+ accuracy consistently for all defect types in production.o Implemented Grad-CAM-based model explainability, ensuring heatmaps consistently highlighted defect areas for better interpretabilityo Impact - Reduced inspection time from 2 hours/lot to 2 minutes/lot, leading to $2M/year in cost savings.Project - Spatial Pattern Recognition• Orchestrated efficient data flow, enhancing data accessibility and facilitating streamlined analysis processes.• Developed Random Forest and Feed-Forward Neural Network models to train on image datasets, accelerating and optimizing the labeling of new images.• Developed and fine-tuned a Convolutional Neural Network (CNN) for image classification, using Tensorflow, enhancing wafer defect patterns detection and image classification.
EDUCATION
Udacity
Secure and Private AI scholarship Challenge, Artificial Intelligence
Udacity
Google Developer Challenge Scholarship, Android Basics
Udacity
Computer Vision Nanodegree, Artificial Intelligence
Karunya Institute of Technology and Sciences
Bachelor of Technology (B.Tech.), Electronics and Communications Engineering
Otto-von-Guericke University Magdeburg
Master of Science (M.S.), Digital Engineering
Udacity
Deep Learning Nanodegree, Artificial Intelligence
SKILLS
ABOUT AKASH ANTONY
Machine Learning Engineer with 5+ years of experience designing end-to-end data processing systems and building high-performance deep learning models. Passionate about leveraging cutting-edge AI technologies to solve real-world challenges and drive industry transformation.Proven expertise in Machine Learning, Deep Learning, and cloud-based deployment of models using PyTorch and TensorFlow on GCP and AWS. Strong foundation in data engineering, statistical modeling, and computer vision.Master’s Thesis: Simulation of network attacks and development of a deep neural network honeypot for cybersecurity applications.Core SkillsProgramming: Python, Java, SQLLibraries & Tools: Numpy, Pandas, Scikit-learn, Matplotlib, OpenCV, SeabornCloud & Platforms: AWS (SageMaker, EC2), GCP, Google BigQuerySpecialized: SLAM, Kalman Filters, PySyft, Flask, LaTeX, Android Development, SAP ERP & SCMML/DL Techniques: Logistic/Linear Regression, SVM, Random Forests, Autoencoders, CNN, RNN, LSTM, GANsComputer Vision: Edge Detection, Hough Transform, Haar CascadesVisionDriving global collaboration and industry transformation through innovative AI solutions that make technology more secure, scalable, and impactful.
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