Job Summary: We are looking for an experienced Computer Vision Engineer to develop, implement, and optimize vision-based AI systems that solve real-world problems. In this role, you will be responsible for designing algorithms, training deep learning models, and deploying solutions that process and interpret visual information. The ideal candidate has a strong foundation in computer vision, machine learning, and software engineering, with experience in deep learning frameworks and image processing. Key Responsibilities: Algorithm Development: Design and implement computer vision algorithms for tasks such as object detection, image segmentation, tracking, and feature extraction. Model Training and Optimization: Train deep learning models (e.g., CNNs, R-CNNs, GANs) for various vision tasks and optimize them for speed, accuracy, and deployment requirements. Data Preparation: Collect, preprocess, and annotate large datasets to ensure high-quality model training. Software and Model Deployment: Develop and deploy vision models into production, ensuring robust performance across devices and platforms. Performance Monitoring: Continuously monitor model performance, troubleshoot issues, and retrain models as needed to ensure ongoing accuracy. Collaboration: Work with cross-functional teams, including software engineers, data scientists, and product managers, to integrate vision capabilities into broader applications. Documentation: Maintain detailed documentation of model architectures, training processes, and deployment configurations. Qualifications: Education: Bachelor's or Master's degree in Computer Science, Electrical Engineering, Robotics, or a related field. Relevant certifications in deep learning or computer vision are a plus. Experience: 3 years of experience in computer vision, machine learning, or a related field, including hands-on work with deep learning. Technical Skills: Deep Learning Frameworks: Proficiency in frameworks such as TensorFlow, PyTorch, or OpenCV. Computer Vision Algorithms: Knowledge of algorithms for image classification, object detection, and semantic segmentation. Programming Languages: Strong skills in Python, with familiarity in C++ as a plus. Tools and Libraries: Experience with libraries like OpenCV, scikit-image, and Dlib, and familiarity with model deployment tools (ONNX, TensorRT). Data Handling: Ability to work with large datasets and proficiency in data handling tools such as pandas, NumPy, and data annotation tools. Soft Skills: Analytical mindset, strong problem-solving skills, and effective communication. Nice-to-Have: Experience with real-time video processing and edge computing. Knowledge of 3D computer vision techniques (SLAM, 3D reconstruction). Familiarity with cloud-based AI/ML services (AWS Sagemaker, Google Vision API). Understanding of model compression and optimization techniques (pruning, quantization) for embedded systems.