Rithika Gurram

Built multi-agent AI workflows in Python to automate processing and prioritization of statewide EMS opioid overdose records, reducing manual triage across high-volume public health data pipelines. Developed reusable Python data-processing modules with Pandas and NumPy to clean, validate, normalize, and transform EMS event data before downstream model inference and analytics.

Built an end-to-end ML pipeline for customer support ticket classification using Python, Pandas, and ensemble models, achieving 85%+ accuracy across high-volume internal queues. Prototyped an Advanced RAG system with LlamaIndex and Pinecone, enabling LLM inference grounded in a deep knowledge base of historical support tickets.

Re-architected data ingestion layers by building Airflow and dbt pipelines processing 1TB+ daily financial datasets, cutting batch runtime by 50%. Built high-throughput FastAPI endpoints with Async Python to ingest unstructured billing streams, handling on-the-fly validation and JSON/Parquet transformation at scale.

Built an end-to-end AI product-digitization platform combining OpenCV and TensorFlow to automate retail data extraction from physical packaging at scale. Developed deep learning text extraction pipelines to process unstructured packaging images, cutting manual data entry effort by 60%.

Master of Science in Applied Data Science from Syracuse University, completed in 2025.

Bachelor of Technology in Computer Science from Indian Institute of Information Technology Dharwad, completed in 2022.