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Yeshwanth Reddy Dareddy
Data Scientist @Abbott
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WORK HISTORY
Data Scientist @Abbott
Irving, TX, US
Leading enterprise-grade AI/ML initiatives across healthcare analytics, focusing on Generative AI, NLP, and predictive modeling to support clinical and operational decision-making.• Designed and deployed LLM-powered intelligent assistants using GPT-4, LangChain, Hugging Face Transformers, and RAG architecture, integrating medical knowledge bases and vector search for contextualized responses.• Built Agentic AI workflows incorporating memory, retrieval agents, and multi-hop reasoning to automate clinical documentation, summarization, and decision support tasks.• Developed and deployed patient attrition prediction models on AWS SageMaker using Random Forest and XGBoost, improving retention by 12 basis points.• Engineered real-time ML pipelines using Kafka, PySpark, and Redshift to process streaming EHR and patient monitoring data for risk surveillance and care management intelligence.• Designed NLP solutions including sentiment analysis, document summarization, topic modeling, and custom Named Entity Recognition (NER) for medical entity extraction.• Implemented text embeddings and semantic search systems using AWS Kendra and GCP Vertex AI to enable intelligent knowledge retrieval and document analysis.• Built CI/CD pipelines with Jenkins and Docker for automated model training, testing, versioning, and cloud deployment across AWS and GCP environments.• Delivered time-series forecasting models using Prophet, TensorFlow, and Statsmodels to predict appointment trends and healthcare service utilization.• Led HIPAA-compliant cloud data migrations (Oracle/DB2 → BigQuery/S3), enabling scalable and secure AI workflows.
EDUCATION
Anna University Chennai
Bachelor of Engineering - BE, Electrical, Electronics and Communications Engineering
University of North Texas
Master of Science - MS, Information Technology
ABOUT YESHWANTH REDDY DAREDDY
This is Yeshwanth, I am a Machine Learning Engineer specializing in Generative AI, NLP, and production-grade ML systems. With over 3 years of experience delivering enterprise AI solutions across healthcare and analytics environments, I focus on building intelligent systems that move from experimentation to scalable deployment. At Abbott Laboratories, I design and implement LLM-powered applications using GPT-4, LangChain, and Retrieval-Augmented Generation (RAG) architectures to automate clinical workflows, knowledge retrieval, and decision support systems.My experience spans the full ML lifecycle from data engineering and feature design to model training, evaluation, deployment, and CI/CD automation on AWS SageMaker and GCP Vertex AI. I have built real-time ML pipelines using PySpark and Kafka, developed NLP systems for summarization and medical entity extraction, and deployed scalable cloud-native AI solutions using Jenkins and Docker. I am passionate about combining machine learning, large language models, and cloud engineering to create measurable business impact through practical, production-ready AI systems.
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