Deep Learning Engineer

Biocliq AI is a deep-tech healthcare company specializing in advanced medical image analytics. Headquartered in Bangalore, India. We build clinically relevant AI solutions that make a real impact in healthcare.

As we scale our medical imaging products, & we are looking for a Deep Learning Engineer with strong hands-on experience in computer vision and image segmentation , preferably in healthcare domains.

• Design, train, and optimize deep learning models for medical image segmentation
• Work with CNN-based architectures such as U-Net, variants of U-Net, and related models
• Handle medical imaging data (X-ray, CT, MRI, ultrasound, etc.) including preprocessing and augmentation
• Collaborate with clinical, product, and engineering teams to translate real-world healthcare problems into AI solutions
• Participate in model evaluation, validation, and deployment pipelines
• Continuously improve model performance, robustness, and efficiency
  

• Strong proficiency in Python
• Hands-on experience with PyTorch or TensorFlow
• Practical experience in computer vision , especially image segmentation
• Solid understanding of CNN architectures and deep learning fundamentals
• Experience working on healthcare or medical imaging projects (academic, internship, or industry)
• Familiarity with basic ML workflows, experimentation, and model evaluation

• Experience with medical datasets (DICOM, NIfTI, etc.)
• Exposure to cloud platforms (AWS, GCP, Azure) for ML workloads
• Basic knowledge of Docker and ML deployment practices
• Participation in research projects, capstone work, or published papers in medical imaging

• 12 years of hands-on experience in deep learning / computer vision
• Strong segmentation project experience (industry, research, or academic projects)
• Candidates with exceptional healthcare-focused projects may be considered even with slightly less experience

• Strong learning mindset and curiosity
• Ability to work independently and take ownership of models
• High standards of ethics, transparency, and responsibility; critical in healthcare AI