AIML Engineer (Medical Imaging)

Cielo Talent

8 - 13 years

18 - 33 Lacs

pune

Posted:2 days ago| Platform:

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Skills Required

dicom aiml medical imaging computer vision python pattern recognition mlops image processing digital image processing

Work Mode

Hybrid

Job Type

Full Time

Job Description

Role & responsibilities

Bachelors, Masters, 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.

Preferred candidate profile

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).

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