Machine Learning Engineer - DICOM/Image Processing (4-8 yrs)

Key Responsibilities

1. Model Development :

- Build and train machine learning and deep learning models to analyze DICOM images, particularly X-rays.

- Develop algorithms to detect common radiological abnormalities (e.g., fractures, tumors, infections) with high accuracy.

- Experiment with state-of-the-art techniques, including CNNs, Transformers, and GANs for medical image analysis.

2. Data Processing :

- Preprocess and augment DICOM datasets (e.g., normalization, resizing, denoising, and segmentation).

- Extract metadata and pixel data from DICOM files using tools like pydico SimpleITK, or dicompyler.

3. Tool Development :

- Collaborate with software engineers to build an interactive radiologist assistant tool.

- Integrate ML models into a user-friendly platform for real-time image analysis and reporting.

4. Research and Optimization :

- Stay updated with advancements in medical imaging and AI research.

- Optimize models for accuracy, performance, and scalability.

- Perform rigorous validation and testing to ensure clinical-grade reliability.

5. Collaboration :

- Work closely with radiologists and domain experts to understand diagnostic workflows and requirement

- Collaborate with backend engineers to ensure seamless integration with PACS and other medical systems.

6. Deployment :

- Deploy AI models in production environments (cloud-based or on-premise).

- Monitor model performance and iterate based on feedback.

Required Skills and Qualification :

1. Education :

- Bachelor's or Master's degree in AI&ML,Computer Science, Data Science or biomedical tech engineering

Experience :

- 2+ years of hands-on experience in machine learning and deep learning.

- Prior experience working with DICOM images and medical imaging datasets.

2. Technical Skills :

- Machine Learning Frameworks : TensorFlow, PyTorch, Keras.

- Medical Imaging Tools : Pydicom, SimpleITK, OpenCV.

- Programming Languages : Python (mandatory), C++ (optional).

- Algorithms : Proficiency in CNNs, object detection (YOLO, Faster R-CNN), a segmentation techniques (UNet, DeepLab).

- Data Processing : Experience with DICOM standards, pixel data extraction, and metadata handling.

- Database Knowledge : Familiarity with databases for storing large medical datasets (e.g., PostgreSQL, MongoDB).

3. Soft Skills :

- Strong problem-solving and analytical skills.

- Excellent communication skills to interact with radiologists and cross-functional teams.

- Ability to work independently and in a team-oriented environment.

4. Preferred :

- Experience in building radiology-specific AI tool

- Knowledge of PACS integration and DICOM network protocols (C-STORE, C-FIND).

- Familiarity with cloud platforms like