Bharani Kumar Yerraballi

Designed, trained, and deployed machine learning and NLP models to support healthcare operations, improving the accuracy of clinical and operational predictions across large-scale patient and claims datasets. Built and productionized LLM-based applications using Retrieval-Augmented Generation (RAG) for secure healthcare data access, integrating vector databases and prompt pipelines to support clinical and business workflows. Developed backend services in Python to integrate AI models into enterprise systems, ensuring low-latency inference, high availability, and seamless interaction with internal APIs and downstream applications. Implemented end-to-end MLOps pipelines including data ingestion, model versioning with MLflow, automated retraining, and monitoring for performance drift in regulated healthcare environments. Collaborated with Responsible AI and compliance teams to validate model behavior, reduce bias, and meet HIPAA and governance standards for AI usage in clinical and pharma

Developed and deployed language-model–based solutions that automated multi-step business tasks, reducing manual processing across document-driven workflows. Fine-tuned transformer models with domain-specific datasets and prompt optimization techniques, improving response relevance and task accuracy by 18–22% in healthcare and retail. Built end-to-end ML and LLM deployment pipelines using Docker, Kubernetes, and cloud ML services, enabling faster model releases and reducing deployment turnaround time by nearly 25%. Integrated AI services with existing enterprise systems through REST APIs and asynchronous workflows, supporting real-time data exchange without disrupting legacy application performance. Applied responsible AI practices by conducting bias checks, access controls, and data validation steps, helping teams meet internal compliance and security standards during production rollouts. Worked closely with product managers, data engineers, and QA teams to translate business requi

Built and supported web applications using JavaScript, PHP, HTML, and CSS, contributing to features used by multiple client teams and supporting applications with a few thousand monthly active users. Worked on backend logic and MySQL databases, writing and optimizing SQL queries that reduced data retrieval time by around 20% for frequently used screens. Coordinated with designers and project managers to understand requirements, clarify changes, and deliver features within sprint timelines across 6–8 active client projects. Identified and fixed bugs through unit testing and debugging, helping reduce production issues by ~25% over successive release cycles. Improved modules by cleaning legacy code and optimizing front-end components, resulting in faster page loads and smoother user interactions. Prepared technical documentation for application workflows, database structures, and deployment steps, helping new team members ramp up faster and reducing dependency on senior developers.