Headline

AI Systems Engineering | Computer Vision & Medical Imaging | Building AI Agents in Healthcare

About

I build AI systems for healthcare. At IIT Delhi's Molecular Imaging and Diagnostics Lab, I lead the development of ScopeX, a clinical application for automated microscopy. It integrates real-time AI-driven diagnostics with a doctor-facing review interface, and I took it from research prototype to alpha release in 4 months by deploying optimised models on NVIDIA Jetson AGX Orin for edge inference. Before IIT Delhi, I worked as a Junior Research Fellow at Ramaiah Institute of Technology, where I built AI solutions for fetal anomaly prediction (88% accuracy on 10,000 scans) and mentored 25 interns across 5 AI projects. I also spent time at the University of Southampton's Biomedical Imaging Unit, working on TB granuloma segmentation using ITK and GPU acceleration. I'm published in IEEE and Springer (Multimedia Tools and Applications, 2026), both on deep learning for fetal contour segmentation. I also hold an ISO 13485 Internal Auditor certification, which gives me a working understanding of medical device quality management. Tech I work with daily: Python, PyTorch, OpenCV, Ultralytics, FastAPI, React, Docker, TensorRT, ONNX, NVIDIA Jetson, GCP/AWS. Open to conversations about applied ML in healthcare, medical imaging research, and clinical AI deployment. Let's Connect !

Location

Greater Delhi Area

AI stack

claude code

claude

codex

mcp

Google ADK

Work experience

Lead Machine Learning Engineer at Indian Institute of Technology, Delhi, Dec 2024 - Present · 1 y 8 mo, New Delhi, - Architected and led development of ScopeX, a full-stack clinical application for automated whole-slide image (WSI) review, designed the React frontend, engineered the FastAPI backend, and packaged the system in Docker for lab deployment. - Built a FastAPI inference server integrating AI analytics into ScopeX, enabling real-time slide analysis and doctor-facing diagnostic review within a single platform. - Designed a hardware-interfacing data pipeline to synchronise microscopy tile acquisition, perform tile stitching, and serve processed slides to clinicians, cutting scanning time by 50% (down to ~10 minutes). - Presented AutoScope/ScopeX at MedTech Expo India 2025 and the India AI Impact Summit 2026. - Authored technical documentation compliant with ISO 13485 and IEC 62304, covering software lifecycle processes, risk management, and quality management.

Machine Learning Engineer at Indian Institute of Technology, Delhi, Sep 2024 - Nov 2024 · 3 mos, - Engineered end-to-end ML training and evaluation pipelines for cervical cancer diagnostics using Python, Docker, and cloud compute (GCP). - Optimised and deployed AI models on NVIDIA Jetson AGX Orin for real-time edge inference, scaling from research prototype to alpha release in 4 months.

Junior Research Fellow at Ramaiah Institute Of Technology, Aug 2023 - Aug 2024 · 1 yr 1 mo, Bengaluru, Karnataka, India, - Led development of AI solutions for real-time fetal anomaly prediction, reaching 88% accuracy on a dataset of 10,000 scans. - Engineered scalable inference pipelines and containerised AI models using Docker for integration into clinical environments. - Collaborated with healthcare professionals on clinical validation and ethical compliance, bridging engineering and medical requirements. - Mentored 25 interns across 5 AI projects, providing technical guidance and code reviews focused on accuracy, testability, and efficiency. - Co-authored research papers on fetal contour segmentation (published in IEEE and Springer) and presented findings at an academic conference.

Project Intern at University of Southampton, Jan 2022 - Sep 2022 · 9 mos, City Of Southampton, England, United Kingdom, Engineered an image processing pipeline using ITK and GPU acceleration for segmentation of tuberculosis granulomas, supporting TB progression research at the university's Biomedical Imaging Unit.

Project Intern at Manipal Academy of Higher Education, Dec 2020 - Jul 2021 · 8 mos, Manipal, India, Developed a prototype AI solution for lung nodule classification, achieving 89% accuracy using a custom architecture trained on analysis of over 1,000 lung CT scans.

Education

Master of Science - MSc, Biomedical/Medical Engineering, University of Southampton

Bachelor of Technology, Biomedical/Medical Engineering, Manipal institute of technology

Public links

github: https://github.com/mohdsinad