Nikunj Doshi
I am Nikunj Doshi a Cloud, Data & AI Consultant, entrepreneur, and startup founder passionate about empowering tomorrow’s leaders. I hold a Master’s in Information Systems from Northeastern University and a Bachelor’s in Information Technology from Thadomal Shahani Engineering College, which have equipped me with a blend of technical expertise and management skills to drive innovation in cloud computing, DevOps, data analytics, and automation.
As the Founder of Achievers Astra, I have guided over 1,500 international students through career development workshops, personalized mentorship, and strategic planning for professional success. As the Director & Regional Head for North America at Abroad Aashaye, I have helped 5,000+ students navigate the U.S. academic journey and built global partnerships to enhance opportunities for international education.
In my corporate career, I worked with Red Hat as a Cloud Solutions Architect and Cloud Site Reliability Engineer, gaining hands-on experience with AWS, OpenShift, distributed cloud architectures, and large-scale automation. My technical toolkit includes Java, Python, C/C++, R, AWS Cloud services, Microsoft Azure, MongoDB, MySQL, Selenium, Ansible, and Git.
I am passionate about fostering engineering excellence, mentoring future leaders, and contributing to discussions on technology, innovation, and global youth leadership. I have been recognized as a LinkedIn Top Voice and invited as a guest speaker at leading universities, sharing insights on career growth, cloud technologies, and developer culture.
Session
Building on but moving far beyond the single-specialty focus of HandRAG, this session examines how Retrieval-Augmented Generation can be engineered to support clinical reasoning across multiple high stakes surgical areas, including orthopedic, cardiovascular, neurosurgical, and plastic surgery domains. Using a corpus of more than 7,800 clinical publications and cross specialty validation studies, the talk highlights practical methods for structuring heterogeneous medical data, optimizing vector retrieval with up to 35% latency gains, and designing prompts that preserve terminology accuracy across diverse subspecialties. Attendees will also learn a three-tier evaluation framework that improved critical-error detection by 2.4×, as well as deployment strategy such as automated literature refresh pipelines and cost-efficient architectures that reduced inference spending by 60% that enable RAG systems to operate reliably in real production healthcare settings.