Vamshi Thanuku

Designed and built enterprise agentic AI systems for clinical documentation and healthcare workflows. Developed RAG pipelines using LLM embeddings and vector databases to improve contextual retrieval. Implemented AI copilots via REST-based microservices and deployed containerized workloads to Azure with CI/CD automation. Collaborated with clinical and product stakeholders in Agile/Scrum to deliver secure, production-ready solutions.

Conducted applied research on generative AI, LLMs, and enterprise AI for healthcare and document intelligence. Built RAG and semantic retrieval pipelines using LangChain, LlamaIndex, and sentence-transformer embeddings, and developed chatbot components with FastAPI and OpenAI APIs. Evaluated LLM quality with prompt engineering and benchmark datasets while supporting experiments with MLflow and reproducible workflows via Airflow. Fine-tuned NLP models with Hugging Face, TensorFlow, and PyTorch and built SQL-based data preparation pipelines.

Developed Python backend microservices and REST APIs for enterprise banking applications and AI-enabled financial platforms. Built document processing workflows using NLP with NLTK and sentence-transformer representations for extraction and classification. Designed SQL data access patterns with PostgreSQL stored procedures and implemented Redis caching for high-throughput processing. Deployed containerized services on AWS and GCP and improved release reliability using Git-based CI/CD with GitHub Actions and Jenkins.