Autonomous Clinical AI Platforms: Agentic Healthcare Systems, Knowledge-Augmented LLMs, and Predictive Clinical Analytics | AGPH Books

Naga Ravi Teja Tadiboina is an AI and Data Engineer with over seven years of experience designing and deploying machine learning and generative-AI systems in demanding, regulated environments, with a particular focus on artificial intelligence for healthcare. His work sits at the intersection of applied AI research and production engineering, centered on building HIPAA- compliant AI/ML pipelines, Retrieval-Augmented Generation (RAG) systems for clinical decision support, and agentic AI that operates reliably where privacy and safety are non-negotiable. His technical expertise spans Python, PySpark, Apache Airflow, LangChain, vector databases, and self-hosted large language models, deployed across cloud platforms including Microsoft Azure, Google Cloud, and Databricks. He works across the full breadth of applied AI - from embeddings and semantic retrieval to multi-agent orchestration and on-premises LLM deployment - with a consistent emphasis on methodological rigor, reproducibility, and trustworthy, reliable AI for high-stakes domains. He holds a Master of Science in Computer Science and has built his career across leading technology and enterprise organizations. A passionate advocate for continuous innovation and knowledge-sharing, he actively engages with the technical community as an author and reviewer, and remains committed to advancing safe, dependable, and impactful applications of artificial intelligence.

Sai Gowtham Tadiboina is an Artificial Intelligence and Machine Learning Engineer with over five years of experience designing, building, and deploying intelligent systems that translate advanced AI research into scalable, production-grade solutions. His work spans the full machine learning lifecycle, from data engineering and model development to deployment and monitoring, with a strong focus on generative AI, large language models (LLMs), and applied machine learning for real-world business problems. His technical expertise includes Python, TensorFlow, PyTorch, and Scikit-learn for model development; LangChain and Retrieval-Augmented Generation (RAG) architectures for building LLM-powered applications; and vector databases such as Pinecone, FAISS, and Chroma for semantic search and retrieval. He has extensive experience deploying and scaling AI/ML systems on cloud platforms including AWS, Microsoft Azure, and Google Cloud, using MLOps practices, Docker, Kubernetes, and CI/CD pipelines to ensure reliable, reproducible model delivery. His work also draws on data engineering tools such as Apache Spark, Airflow, and SQL to build the robust pipelines that power AI systems at scale. A committed advocate for innovation and knowledge-sharing, Sai Gowtham actively contributes to the AI community as an author, researcher, and mentor, working to advance the responsible and impactful application of artificial intelligence and machine learning across industries. He continues to pursue emerging areas of AI, including agentic systems, multi-agent orchestration, and trustworthy AI, with the goal of building technology that is both innovative and reliable.