AI/ML Engineer

Exp : 8 to 10 years
Location : PuneKey ResponsibilitiesDesign, 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.