Rajeshwari Sah
Rajeshwari Sah, Machine Learning Engineer at Apple
Sunnyvale, California, United States
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I am a Machine Learning Engineer with specialized expertise in fine-tuning and aligning Large Language Models (LLMs), including proprietary models like CLINICALGPT, as well as LLAMA, ALPACA, and MISTRAL.
Currently a Machine Learning Engineer at Apple, I previously held a Lead Applied Scientist role at Fresh Gravity and Lifio.ai, where I focused on building AI agents, document extractors, and protocol document generators for complex, high-stakes domains.
My technical stack is geared toward production-scale AI, spanning Python, PyTorch, Golang, TensorFlow, and LangChain, with deep experience in vector databases like Pinecone, ChromaDB, Qdrant, and FAISS. I hold a Master of Science in Computer Science from UC San Diego, where I also served as a Graduate Teaching Assistant for algorithms and machine learning. I am passionate about translating cutting-edge AI research into robust, business-driving solutions.
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Building an Agentic AI Medical Scribe with LangGraph: From Voice to Structured SOAP Notes
Medical documentation continues to be one of the biggest productivity bottlenecks in healthcare. In this hands-on workshop, participants will learn how to design and deploy an agentic AI medical scribe that transforms real or simulated doctor–patient conversations into structured SOAP (Subjective, Objective, Assessment, Plan) notes.
Using LangGraph, we’ll build a dynamic, multi-agent system where each node performs a specialized task and communicates through a shared memory graph:
Transcription Agent — converts clinical dialogue into text using a speech-to-text model.
Information Extraction Agent — identifies key clinical entities such as symptoms, vitals, and diagnoses.
Summarization Agent — generates structured SOAP notes and validates internal consistency through a self-reflective loop.
Attendees will see how LangGraph enables state-aware orchestration and real-time coordination between these agents, how to integrate retrieval grounding using clinical ontologies (ICD-10, SNOMED), and how to measure performance with F1, ROUGE-L, and readability metrics.
By the end, participants will deploy a lightweight, production-ready prototype that demonstrates how agentic workflows can automate time-consuming documentation tasks, while remaining transparent, auditable, and compliant.
Rajeshwari Sah
Rajeshwari Sah, Machine Learning Engineer at Apple
Sunnyvale, California, United States
Actions
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