Arul JC

Design and deploy scalable machine learning and deep learning solutions to automate document processing and support enterprise analytics workflows. Develop and optimize neural network and transformer-based models for document classification, information extraction, and predictive analytics using PyTorch and TensorFlow. Build end-to-end ML pipelines including data preprocessing, feature engineering, model training, evaluation, and production deployment on AWS infrastructure. Implement model monitoring, performance optimization, and automated retraining pipelines to ensure reliability and scalability. Collaborate cross-functionally with engineering and product teams to deliver production-ready ML systems that improve operational efficiency and enable data-driven decision-making.Design and deploy scalable machine learning and deep learning solutions to automate document processing and support enterprise analytics workflows. Develop and optimize neural network and transformer-based models for document classification, information extraction, and predictive analytics using PyTorch and TensorFlow. Build end-to-end ML pipelines including data preprocessing, feature engineering, model training, evaluation, and production deployment on AWS infrastructure. Implement model monitoring, performance optimization, and automated retraining pipelines to ensure reliability and scalability. Collaborate cross-functionally with engineering and product teams to deliver production-ready ML systems that improve operational efficiency and enable data-driven decision-making.

Developed and deployed scalable machine learning and deep learning solutions for enterprise SaaS applications, focusing on NLP, document intelligence, and AI automation. Designed and fine-tuned transformer-based neural network models, including large language models, for text classification, content filtering, and information extraction. Built end-to-end ML pipelines covering data preprocessing, feature engineering, model training, evaluation, and production deployment on cloud infrastructure. Implemented model monitoring, benchmarking, and optimization techniques to improve performance, reliability, and scalability. Collaborated with cross-functional engineering and product teams to integrate production-ready ML systems that enhanced automation, security, and operational efficiency.

Developed and deployed scalable machine learning models and data pipelines to support predictive analytics, forecasting, and business intelligence applications. Built end-to-end ML pipelines including data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment using Python and AWS cloud infrastructure. Designed feature pipelines and optimized machine learning models to improve prediction accuracy and support data-driven decision-making. Implemented automated ETL workflows and data validation processes to ensure data quality and reliability. Collaborated with cross-functional teams to integrate production-ready ML solutions and improve system performance and operational efficiency.

GPA: 3.30/5.00

Relevant Coursework:
Machine Learning, Deep Learning, Neural Networks, Data Structures and Algorithms, Statistical Modeling, Probability and Statistics, Python for Data Science, Database Management (SQL), Data Mining, Data Visualization, Artificial Intelligence, Cloud Computing, Predictive Analytics

• Relevant Coursework: Data Systems, Data Algorythms, Linear algebra, Object Oriented Programming, Databases Management, Discrete Mathematics, Operating Systems, Computer Networks, Machine Learning, Data Mining, Cloud computing
• GPA: 8.00 / 10