Job Specific Information
Title:
AI/ML Engineer – Medical Imaging
Location:
Pune/Gurugram
Purpose Statement
The AI/ML Engineer will lead the design and implementation of advanced imaging algorithms for a next-generation healthcare platform focused on 3D reconstruction from medical modalities such as TEE and CT. This role involves applying state-of-the-art machine learning, deep learning, and computer vision techniques to enhance medical image understanding, segmentation, and reconstruction. The candidate will collaborate with cross-functional teams including frontend, backend, QA, DevOps, and clinicians to build AI-driven imaging solutions.
- Design, develop, and optimize AI/ML algorithms for medical image analysis, segmentation, and 3D reconstruction from TEE and CT images.
- Research and implement advanced deep learning architectures including CNNs, GANs, VAEs, and Diffusion Models for medical imaging tasks.
- Develop robust 3D reconstruction pipelines from 2D image data and multi-view geometries, tailored to medical imaging workflows.
- Perform multimodal image registration (CT-CT, CT-MRI, Fluro-Endo, 2D-3D) and develop tools for alignment, calibration, and fusion.
- Enhance and denoise medical images using advanced computer vision and AI-based enhancement techniques.
- Work extensively with DICOM data, integrating with PACS systems for data ingestion and retrieval.
- Collaborate with teams for dataset curation, labeling, and ground truth generation.
- Develop scalable training and inference pipelines on cloud platforms (AWS preferred; Azure/GCP acceptable).
- Ensure reproducibility and traceability in experiments using MLOps practices (Docker, MLflow, or similar).
- Collaborate with software engineers to integrate AI components into production-grade imaging applications.
- Document research findings, maintain version-controlled repositories, and contribute to technical publications or IP filings.
- Stay up-to-date with emerging trends in AI, computer vision, and medical imaging technologies.
- Bachelor’s, Master’s, or Ph.D. in Computer Science, Data Science, Biomedical Engineering, or related field with focus on AI, ML, or Computer Vision.
- 8+ years of hands-on experience in AI/ML model development with strong exposure to computer vision and imaging applications.
- Expert-level proficiency in Python and deep learning frameworks such as PyTorch and TensorFlow.
- Experience in 2D and 3D medical imaging (CT, MRI, Ultrasound, TEE) and DICOM data handling.
- Strong understanding of 3D geometry, camera calibration, stereo vision, and multi-view reconstruction.
- Experience in segmentation, registration, and object tracking within medical image contexts.
- Proficiency with classical computer vision techniques (OpenCV, PCL, feature detection, structure-from-motion, SLAM, etc.).
- Knowledge of generative and reconstruction models (GANs, VAEs, Diffusion Models) and fine-tuning methods for domain-specific applications.
- Experience with data preprocessing, augmentation, and pipeline automation for large-scale medical datasets.
- Familiarity with MLOps, containerization (Docker), and deployment workflows for cloud and edge environments.
- Experience using cloud platforms (AWS, Azure, or GCP) for model training and large dataset management.
- Strong software engineering fundamentals — version control (Git), testing, CI/CD, and documentation practices.
- Excellent analytical, communication, and collaboration skills with a strong commitment to quality and compliance in healthcare development.
Preferred / Secondary Skills
- Experience in endoscopy imaging, image mosaicing, and fusion with ultrasound imaging.
- Experience in 3D visualization, rendering, and medical image annotation tools.
- Knowledge of reinforcement learning or self-supervised learning techniques for imaging applications.
- Background in signal processing or physics-based imaging reconstruction methods.
- Exposure to regulatory and quality systems in medical device software development (e.g., ISO 13485, IEC 62304).
Quality System Requirements
- Demonstrates a primary commitment to patient safety and product quality by maintaining compliance to the Quality Policy and all other documented quality processes and procedures.
- Establishes and promotes a work environment that supports the Quality Policy and Quality System.
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