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Associate Director at Qure.ai || LLMs for Healthcare || AI in Healthcare || Generative AI
I'm Sahil, an AI Scientist passionate about using technology to make a positive impact in healthcare. Currently, I'm working at Qure AI, where I focus on improving how we use language-vision models for medical imaging. At Qure we are building foundation models using Llama, Blip-2, and miniGPT4 in medical domain. One of my recent achievements is the development of qMSK, a product at Qure AI that can detect fractures in musculoskeletal X-rays. It took us nine months to build it from scratch, and now it's being deployed in various countries. We also did the clinical evaluation at Massachusetts General Hospital and Erasmus University. We even presented our work at the American College of Radiology (ACR) conference, and it's currently undergoing FDA clearance processes. I've also worked on creating synthetic medical images for data augmentation and developed teacher-student models for faster CPU inference. Some of this work is being considered for publication in The Lancet journal. Before diving into the industry, I spent time in academic research, exploring areas like adversarial training, unsupervised monocular depth estimation, and medical image segmentation. My research has been shared in conferences like ICTAI, and I have submissions under review at respected medical imaging conferences such as RSNA, EUSOMII, and ACR. I graduated with a solid foundation in electrical engineering and computer science from IISER Bhopal. With over two years of industry experience, I'm humbled to be recognized as someone with expertise in AI for medical imaging, particularly in applying innovative approaches to language and vision models.
IISER Bhopal
Master of Science - MS, Electrical Engineering And Computer Science (EECS)
January 1, 2017 – January 1, 2022
Qure.ai
Associate Director - Data Science
April 1, 2025 – Present
Qure.ai
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Cultural Fit Analysis
The candidate has a strong background in AI/ML research and development, primarily within the medical imaging domain. While this demonstrates deep specialization, the target role is 'Backend Engineer', which typically requires a broader set of skills in system architecture, API design, database management, and general software engineering principles. The candidate's experience is heavily skewed towards data science and AI model development, which may not directly align with the core responsibilities of a traditional backend engineering role. The diversity of projects is within the AI/ML domain, but not necessarily across different software engineering paradigms. This suggests a potential mismatch with a pure backend engineering cultural fit, though their strong technical foundation could be transferable with targeted upskilling.
Soft Skills & Operational Fit
The candidate's resume highlights significant contributions to product development and research, indicating strong problem-solving and technical leadership potential. However, without specific psychometric or communication test results, it is difficult to assess soft skills and operational fit comprehensively. The descriptions of past roles suggest an ability to work on complex, multi-faceted projects.