Dhruva Teja Chukkapalli

Designed and deployed AI-driven credit risk models using MLflow, Airflow, and Kubeflow, enabling scalable and automated decision-making across hybrid cloud infrastructure. Integrated generative AI capabilities using Lang Chain, LLM APIs, and vector databases like Pinecone and Weaviate, delivering contextual advisory and support for digital banking users.

Developed AI models using Python, TensorFlow, and Scikit-learn to predict patient appointment no-shows, optimize clinic staffing, and enhance resource allocation for Medicaid beneficiaries. Deployed predictive models in production using Docker and Kubernetes, ensuring reliability and scalability of healthcare AI applications.

Developed predictive models using Python, Scikit-learn, and TensorFlow to assess policyholder risk, improve underwriting decisions, and reduce claim fraud in health insurance operations. Engineered scalable data warehouse solutions using Google Big Query, Snowflake, and Amazon Redshift to support advanced analytics and regulatory reporting.

Managed structured and unstructured pharmaceutical research and clinical trial data using SQL, Cassandra, and MongoDB, enabling efficient storage and retrieval of compound, patient, and experimental datasets. Deployed machine learning models using Flask, Docker, Kubernetes, and MLflow to ensure scalable, production-grade applications for clinical outcome predictions and personalized medicine recom

Completed a Master's degree focusing on Big Data and Information Technology. Gained expertise in managing and analyzing large datasets, and developing information systems.