Raghav Kodaganti

AI/ML Engineer @ Intermountain Health

About

As an AI/ML Engineer and Data Scientist with 6+ years of experience across the U.S. and India, I specialize in designing, developing, and deploying end-to-end Generative AI and Machine Learning systems that transform large-scale, complex data into strategic intelligence. With a Master’s in Computer Science and hands-on experience spanning healthcare, finance, and automotive domains, I bridge the gap between advanced AI research and scalable enterprise applications.At Intermountain Healthcare, I architected multi-agentic LLM-based RAG frameworks leveraging GPT-4, BERT, LangChain, and LlamaIndex, achieving over 90% retrieval accuracy and significantly accelerating compliance documentation automation. At FINRA, I fine-tuned GPT and T5 transformer models to personalize digital communications—cutting operational costs by 60% and improving engagement by 20%. Earlier at TCS, I built and optimized AI-driven credit risk and fraud detection systems using PySpark, MLflow, and AWS SageMaker, enhancing prediction accuracy and reducing risk exposure.I’m passionate about multi-agentic AI architectures, deep learning, and MLOps-driven deployment—with core expertise in LangChain, Hugging Face, PyTorch, FastAPI, and cloud-native AI workflows. My mission is to build explainable, production-ready AI solutions that integrate seamlessly with enterprise systems and accelerate decision-making. Always open to collaborating with innovators driving the future of Generative AI and Machine Learning.

Country

United States

City

Charlotte

Industry

Computer Software

Skill

LangChain, GPT-3, Artificial Neural Networks, OpenAI API, Artificial Intelligence (AI), Regression Analysis, Regression Models, Visual Studio, MLOps, PyTorch, Natural Language Processing (NLP), Scikit-Learn, Apache Kafka, PySpark, Software Infrastructure, Hibernate, PostgreSQL, Tailwind CSS, MySQL, MongoDB

Experience

AI/ML Engineer

Intermountain Health

2024-8 - 2026-1 · 1 yr 6 mos

Salt Lake City Metropolitan Area

• Developed and deployed end-to-end machine learning applications, enhancing operational efficiency. • Contributed to iDoc, leveraging NLP, computer vision, and AutoML frameworks for innovative solutions. • Engineered and optimized ML models using Decision Trees, Random Forest, XGBoost, and SVM for predictive analytics. • Integrated multi-agentic LLM architectures for advanced content generation and summarization using GPT-4 and BERT. •Deployed multiple ML and web applications using Django, FastAPI on cloud platforms including Azure and AWS hands-on experience with Databricks, Azure ML, and SageMaker for scalable ML workflows.

Machine Learning Engineer

FINRA

2022-7 - 2024-7 · 2 yrs 1 mo

• Fine-tuned GPT-3 models for personalized cold email generation, increasing open rates by 20% and response rates by 4%. • Developed an in-house T5 transformer model, reducing project costs by 60% and minimizing reliance on external APIs. • Collaborated on Predictive Demand Generation initiatives using ANN, achieving an AUC score of 0.81. Built and deployed ensemble models on AWS SageMaker to evaluate creditworthiness, enhancing predictive accuracy and reducing false positives. • Developed a customized AI model for shopper profiling to deliver personalized web-based product recommendations and improved user experience.

Associate Data Scientist

Tata Consultancy Services

2019-8 - 2021-12 · 2 yrs 5 mos

Bengaluru, Karnataka, India

• Developed production-grade credit risk assessment models using Python, Scikit-learn, and XGBoost, achieving a 23% reduction in loan default rates. • Created NLP-based document processing pipelines with spaCy and BERT for financial contract classification, enhancing compliance accuracy by 30%. • Implemented an MLOps automation framework with MLflow and AWS SageMaker, reducing deployment cycles from 14 days to 48 hours.

Software Development Engineer

Mercedes-Benz Research and Development India

2018-7 - 2019-7 · 1 yr 1 mo

Greater Bengaluru Area

• Conducted in-depth analysis of various regression models, improving accuracy by 22% using advanced metrics. • Deployed regression models as RESTful APIs with FastAPI, enabling real-time inference and system integration. • Collaborated in a cross-functional team, ensuring timely delivery and reproducible results through effective version control and documentation.