Vinitha Patel
Ai Ml Engineer @UnitedHealth Group
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WORK HISTORY
Ai Ml Engineer @UnitedHealth Group
US
Developed predictive risk models using 1.4M+ patient records, increasing care prioritization accuracy by 18% through feature engineering and gradient-based classification techniques.• Implemented automated ML pipelines across 11 clinical workflows, reducing manual deployment effort by 36% via containerized inference services and scheduled validation checks.• Enhanced clinical text processing over 3.9M physician notes, improving medical entity recognition precision by 17% using transformer-based language modeling.• Established centralized experiment tracking for 45+ model runs, lowering redundant retraining cycles by 28% through versioned artifacts and controlled evaluation baselines.• Optimized inference latency for real-time scoring on 22 applications, decreasing response time by 31% through batch prediction tuning and memory-efficient execution.• Standardized monitoring rules across 7 production models, reducing undetected performance drift incidents by 24% using threshold-based alerts and historical trend analysis.
EDUCATION
University of Central Missouri
Master of Science - MS, Computer Science
Malla Reddy Engineering College & Mangement Sciences
Bachelor of Technology - BTech, Information Technology
ABOUT VINITHA PATEL
I’m an AI/ML Engineer with 3+ years of experience designing, developing, and deploying machine learning solutions across predictive analytics, NLP, and Generative AI.My work focuses on building scalable, production-grade ML systems that transform complex datasets into measurable business and clinical impact.At UnitedHealth Group, I’ve contributed to: Improving care prioritization accuracy using predictive risk models trained on 1.4M+ patient records Enhancing medical NLP performance across 3.9M+ physician notes Deploying automated ML pipelines across clinical workflows Reducing inference latency and improving model monitoringI specialize in:• Machine Learning & Predictive Modeling• NLP & Transformer-based architectures• Generative AI (RAG, embeddings, LLM workflows)• MLOps & Model Deployment• Cloud ML on AWS & AzureI enjoy solving real-world problems at the intersection of AI, healthcare, and scalable systems.Open to opportunities in Machine Learning, NLP, Applied AI, and Generative AI Engineering.
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