Rajeshwari Sah
Rajeshwari Sah, Machine Learning Engineer at Apple
Sunnyvale, California, United States
Actions
Rajeshwari Sah is a Machine Learning Engineer at Apple, where she works on production-scale Agentic AI and Retrieval-Augmented Generation systems powering next-generation intelligent experiences. She has led the development of multilingual, voice-enabled LLM frameworks and advanced agentic orchestration pipelines that significantly improved enterprise efficiency and system reliability.
Rajeshwari’s expertise spans fine-tuning techniques such as RLHF and DPO, multi-agent collaboration, and alignment-driven evaluation frameworks. She has delivered high-impact AI solutions across healthcare, finance, and e-commerce, focusing on workflow automation, process optimization, regulatory document intelligence, and AI-driven document generation. Her work enables knowledge-driven automation and modern policy and decision-support systems for global enterprises.
She is a strong advocate for responsible AI deployment, emphasizing innovation that remains auditable, reliable, and culturally consistent across markets. Rajeshwari holds a Master of Science in Computer Science from UC San Diego and continues to contribute to the evolving landscape of applied AI.
Area of Expertise
Topics
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
Please note that Sessionize is not responsible for the accuracy or validity of the data provided by speakers. If you suspect this profile to be fake or spam, please let us know.
Jump to top