Pulluru Rupini Priya

Led projects using Azure cloud services to handle large volumes of structured and unstructured healthcare data to support ML pipelines. Automated CI/CD of ML models with Azure DevOps and Azure ML Pipelines. Designed and deployed supervised and unsupervised ML models for classification and segmentation problems. Applied ensemble techniques and built neural networks including CNNs and LSTMs for image classification and time series forecasting. Developed advanced NLP models leveraging BERT and spaCy for customer feedback and chatbot implementations. Collaborated with DevOps teams to streamline ML workflows using Docker, Jenkins, and Git, enhancing production scalability and reliability.

Orchestrated ETL pipelines with AWS Glue and Step Functions to support near real-time ML model data needs. Integrated MLOps pipelines using GitHub Actions and Jenkins to automate deployment and monitoring. Applied advanced statistical and deep learning models in R, TensorFlow, and PyTorch for customer behavior analysis, image recognition, and sentiment analysis. Built scalable data querying solutions using Spark SQL and NoSQL databases. Managed Kubernetes clusters for scalable model serving and automated Tableau dashboards for business insights. Supported real-time data streaming via Kinesis and AWS Lambda integrating data into ML workflows.

Managed ML model retraining workflows leveraging Azure Data Factory and Azure Monitor. Developed interpretable neural networks using PyTorch and built interactive R Shiny dashboards for business KPIs. Created reusable Python modules for scalable analytics and collaborated with data engineering to construct data lakes and NoSQL storage solutions. Automated CI/CD workflow integration with Docker and Jenkins and implemented Power BI role-level security to secure enterprise ML reporting. Built TensorFlow Keras custom neural network architectures with advanced regularization and callbacks for robust model performance.