# Neeraj J. > AI/ML Engineer 🤖 | Generative AI & LLMs (RAG, NLP) 🧠 | Building Scalable AI Systems 🚀 | MLOps & Cloud (AWS, Docker) ☁️ | Turning Data into Impact 📊 Location: United States, United States Profile: https://flows.cv/neerajj AI/ML Engineer with 3+ years of experience building scalable, production-grade machine learning and generative AI solutions across healthcare and legal domains. I specialize in NLP, LLMs, and RAG-based systems, leveraging tools like LangChain, Hugging Face Transformers, and vector databases to power intelligent applications such as conversational AI and semantic search systems 🤖. My work focuses on delivering measurable impact—improving information retrieval accuracy, reducing model hallucinations, and enabling real-time, high-availability AI services. I have strong hands-on experience across the full ML lifecycle, from data processing and feature engineering with PySpark to model development using TensorFlow, PyTorch, and Scikit-learn, and deployment using FastAPI, Docker, and cloud platforms like AWS ☁️. I’m also deeply invested in MLOps practices, utilizing MLflow, CI/CD pipelines, and monitoring frameworks to ensure reliable and continuously improving systems 📈. Passionate about applying AI to solve real-world problems, I aim to build intelligent, efficient, and impactful solutions that bridge the gap between advanced research and practical implementation 🚀. ## Work Experience ### AI/ML Engineer @ CVS Health Jan 2025 – Present | Tallahassee, FL At CVS Health, I’ve been focused on building real-world AI systems that directly improve patient experience and clinical efficiency. I led the development of a healthcare conversational AI assistant powered by LLMs and RAG pipelines using LangChain and OpenAI APIs. This system enabled accurate, context-aware responses and improved patient query resolution while reducing manual workload for support teams. One of my key contributions was designing a semantic search layer using vector databases and embedding models, which improved information retrieval relevance by 30% across large healthcare knowledge bases. I also architected scalable microservices using FastAPI and deployed them on AWS, ensuring high availability and real-time inference performance. On the data side, I built PySpark-based pipelines with AWS Glue to process large volumes of structured and unstructured healthcare data, enabling robust feature engineering. I also implemented prompt engineering and fine-tuning strategies that reduced LLM hallucinations by 25%. To ensure reliability, I introduced MLflow-based monitoring and evaluation frameworks, enabling continuous model improvement and compliance tracking. ### ML Engineer @ LexisNexis Jan 2020 – Jan 2023 t LexisNexis, I worked on building scalable machine learning solutions to extract insights from complex legal documents. I developed an end-to-end document intelligence platform that automated classification and information extraction, significantly reducing manual processing time for legal teams. I built and optimized machine learning models using Scikit-learn and XGBoost, and later enhanced performance by integrating transformer-based NLP models from Hugging Face—improving classification accuracy by 18%. My work involved designing robust preprocessing and feature engineering pipelines using Pandas and NumPy to handle highly unstructured legal text. To scale the system, I engineered distributed data pipelines using PySpark, enabling batch and near real-time processing of large legal datasets. I also deployed models as REST APIs using FastAPI and Docker, reducing response latency by 25% and improving integration across internal platforms. Additionally, I implemented MLOps workflows using MLflow and CI/CD pipelines, which streamlined experimentation, versioning, and deployment. Continuous monitoring and retraining improved model reliability by 20% over time. ## Education ### Master's degree in Computer Science-Data Science Florida State University ### Bachelor of Engineering - BE in Information Technology D Y Patil International University, Akurdi, Pune ### Junior College in Science Jai Hind Junior College ### Secondary School Education Dr. D. Y. Patil Vidyapeeth ## Contact & Social - LinkedIn: https://linkedin.com/in/neeraj-jawahirani-71841a12a - Portfolio: https://neerajj.netlify.app - Portfolio: https://neerajjawahirani.medium.com/ --- Source: https://flows.cv/neerajj JSON Resume: https://flows.cv/neerajj/resume.json Last updated: 2026-04-17