Machine Learning Engineer (YOLO Specialist)

Brief DescriptionExperience: 3 - 4 years We are seeking a talented and experienced Machine Learning Engineer with expertise in YOLO (You Only Look Once) object detection models. The ideal candidate will have a strong background in deep learning, computer vision, and machine learning techniques. You will be responsible for developing, optimizing, and deploying computer vision models, with a particular focus on real-time object detection using YOLO. Key Responsibilities:

Design, develop, and optimize deep learning models, with a focus on YOLO object detection architectures (YOLOv3, YOLOv4, YOLOv5, etc.). Train and fine-tune models on large-scale datasets for real-time object detection tasks. Collaborate with data scientists and software engineers to integrate YOLO-based solutions into production pipelines. Research and implement state-of-the-art techniques to improve object detection accuracy, speed, and robustness. Preprocess, augment, and annotate datasets to improve model performance. Monitor model performance and accuracy and iterate as needed to maintain efficiency. Stay up to date with the latest trends and advancements in computer vision and deep learning, particularly related to YOLO models. Deploy YOLO-based models in production environments and optimize for real-time performance. Write efficient, clean, and maintainable code for ML models, using frameworks such as TensorFlow, PyTorch, and OpenCV. Preferred Skills YOLO Expertise: Proficient with YOLOv3, YOLOv4, YOLOv5, YOLOv8. Machine Learning & Deep Learning: Strong grasp of deep learning architectures and ML algorithms, familiarity with deep learning frameworks such as TensorFlow, PyTorch and Keras, along with data visualization skills. Computer Vision: Expertise in object detection, image classification, image preprocessing, and augmentation, with familiarity in OpenCV and other relevant libraries. Programming Languages: Python (NumPy, Pandas, Scikit-learn), optional C++. Model Deployment & Version Control: Experience with Docker, Kubernetes, cloud platforms (AWS, GCP, Azure), Git, and CI/CD pipelines.