Responsibilities
- Explore and develop innovative Artificial Intelligence (AI) algorithms for healthcare applications
- Create and refine AI algorithms for pre- and post-processing of images and videos, focusing on data from various imaging modalities
- Develop and implement machine learning and deep learning techniques for segmentation, classification, and statistical modeling.
- Demonstrate expertise in image processing, object detection, segmentation, and classification.
- Proficient in Python programming
- Possess a strong understanding of algorithms and frameworks such as TensorFlow, PyTorch, and Keras
- Experienced with version control systems (e.g., Git) and software development practices
- Develop and Optimize Computer Vision Models: Design, train, and fine-tune DL models for real-world applications
- Data Preparation & Engineering: Gather, clean, and preprocess large-scale image and video datasets for training and evaluation of computer vision models
- Experimentation & Model Evaluation: Conduct A/B testing and assess model performance using quantitative metrics (e.g., IoU, mAP, precision, recall)
- Research & Innovation: Stay updated with the latest advancements in computer vision, deep learning, and related technologies
- Deployment & Scaling: Work with ML engineers to deploy models into production environments using cloud platforms (AWS, Azure) and frameworks like TensorFlow, PyTorch, and OpenCV
- Collaboration & Communication: Work closely with cross-functional teams to integrate computer vision solutions into business processes and applications.
Requirements
- Bachelor’s or master’s Degree: In computer science, AI, Data Science, Machine Learning, or a related field
- Experience: 3+ years in machine learning, deep learning, or AI research, with at least 1 year of hands-on experience in developing computer vision-based AI applications
- Programming Proficiency: Strong proficiency in Python and ML frameworks like TensorFlow and PyTorch
- Domain Knowledge: Knowledge of computer vision, natural language processing (NLP), or multimodal AI applications
- Technical Skills: Familiarity with computer vision techniques and fine-tuning of models
- Problem-Solving Skills: Strong problem-solving skills and the ability to work in a fast-paced, research-driven environment
- MLOps Tools: Hands-on experience with MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes)
- Ethical AI: Understanding of ethical AI and bias mitigation in computer vision models.
Nice to Have
- Publications and Contributions: Strong publication record or contributions to open-source AI projects.
