Ajay Kumar Jagu

Machine Learning Ai Engineer @Cardinal Health

Charlotte, NC, US

Built and deployed Health care analytics platform for processing both structured and unstructured healthcare data, reducing manual effort by 40% and improved the accuracy by 20%.• This platform involves in developing ML models like Random Forest for disease prediction progression for patient disease prediction automation and achieved 92% overall accuracy.• Designed RAG system for Medical Text Summarization Chatbot to enhance the output response from the LLM using Pinecone Vector database for the doctors.• Integrated MLOps practices using Docker, Airflow, and CI/CD pipelines, cutting model deployment time by 40% and ensuring consistent version control and rollback capabilities.• Trained and optimized ensemble models (Random Forest and XG Boost) for disease prediction, reducing false positives by 16% and improving model F1-score by 0.20.• Deployed real-time model monitoring tools using Prometheus and Grafana, enabling anomaly detection and model drift alerts with under 5-minute latency.• Conducted A/B testing for model updates, resulting in a 12% increase in customer engagement through personalization algorithms.• Documented all pipelines, APIs, and model architecture in compliance with internal audit standards, improving audit turnaround time by 35%. • Optimized cloud resource allocation for ML workloads, reducing AWS inference costs by 18% while maintaining SLA requirements.

University of New Haven

Master's degree