Showri Niharika Yeruva

Led design and deployment of AI solutions including RAG-based intelligent assistants leveraging Azure OpenAI and LangChain for automating customer queries. Developed and fine-tuned transformer models for document summarization, risk assessment, and sentiment analysis while ensuring compliance with financial regulations. Engineered multi-modal generative AI systems combining text and financial data for fraud detection. Built MLOps pipelines on Azure DevOps supporting CI/CD, automated retraining, monitoring, and model versioning. Integrated Azure Cognitive Services for OCR, speech-to-text, and NLP pipelines. Optimized vector search with Pinecone and FAISS to reduce latency for AI chatbots. Partnered with stakeholders to deliver AI solutions aligned with enterprise priorities and deployed scalable AI microservices with Docker and Kubernetes.

Built LLM-based summarization pipelines for clinical documents to speed researcher insights. Developed generative AI chatbots providing real-time health information to improve patient engagement. Applied semantic retrieval and vector search on biomedical datasets for AI decision support. Designed explainability frameworks using SHAP and LIME for regulatory compliance. Developed MLOps pipelines for continuous monitoring and retraining to handle patient data drift. Integrated Azure Cognitive Services for medical image classification and anomaly detection. Ensured HIPAA and FDA compliance by embedding privacy-preserving techniques and model governance. Built personalized recommendation engines for treatment options and designed ML workflows for predictive healthcare analytics.

Designed scalable ETL pipelines with Azure Data Factory and Databricks integrating diverse data sources into Azure Synapse. Built PySpark workflows for data cleansing and transformation to support analytics and ML workloads. Implemented centralized data lakes with Delta Lake for governance and scalability. Automated pipelines with dynamic triggers and error handling. Collaborated with AI engineers on feature dataset preparation and preprocessing. Designed secure data governance frameworks for compliance. Developed Kafka and Event Hub pipelines enabling real-time high-throughput data ingestion. Authored SQL and Python validation frameworks for data quality assurance. Created CI/CD pipelines for automated deployment of data workflows.