Computer jobs in United Kingdom

Are you passionate about teaching machines to "see" and understand the world through visual data? Do you thrive on developing innovative algorithms and models that allow computers to process and interpret images and videos, bringing artificial vision to life? If you're excited about solving real-world problems through cutting-edge computer vision techniques, then our client has the perfect opportunity for you. We’re looking for a Computer Vision Engineer (aka The Visionary Technologist) to design, implement, and optimize computer vision systems that power transformative applications across various industries.

As a Computer Vision Engineer at our client, you’ll work closely with data scientists, AI researchers, and product teams to create vision-based AI systems that interpret visual data with accuracy and efficiency. From image recognition and object detection to video analysis and augmented reality, you’ll help push the boundaries of what's possible in machine perception.

Key Responsibilities:

1. Develop Computer Vision Algorithms: Design and implement advanced computer vision algorithms for tasks such as object detection, facial recognition, image segmentation, and motion tracking. You’ll work with frameworks like OpenCV, TensorFlow, PyTorch, or YOLO to build scalable vision systems.
2. Build and Train Deep Learning Models: Create and train deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to solve complex computer vision problems. You’ll experiment with architectures like ResNet, VGG, and EfficientNet to optimize performance.
3. Image and Video Processing: Develop solutions for processing and analyzing large volumes of visual data from images and videos. You’ll build real-time video analytics systems, work on edge computing applications, and optimize image processing workflows for performance and scalability.
4. Data Annotation and Preprocessing: Prepare and preprocess datasets for training and evaluation. You’ll perform data augmentation, image normalization, and labeling to create high-quality datasets that improve model accuracy and robustness.
5. Deploy Computer Vision Models in Production: Collaborate with software engineers and DevOps teams to deploy computer vision models into production environments. You’ll use tools like Docker and Kubernetes to ensure that models are scalable, efficient, and integrated into real-world applications.
6. Experiment with Emerging Technologies: Stay up to date with the latest advancements in computer vision, deep learning, and AI. You’ll explore cutting-edge technologies such as generative adversarial networks (GANs), 3D vision, augmented reality (AR), and edge AI to push the boundaries of innovation.
7. Collaborate with Cross-Functional Teams: Work closely with product managers, data engineers, and designers to integrate computer vision capabilities into products. You’ll ensure that the solutions you build align with business goals and deliver real-world impact.

Required Skills:

• Computer Vision Expertise: Strong knowledge of computer vision techniques, including image classification, object detection, image segmentation, and feature extraction. You’re experienced with libraries and frameworks like OpenCV, TensorFlow, PyTorch, or Keras.
• Deep Learning Proficiency: Expertise in deep learning techniques, particularly in convolutional neural networks (CNNs), RNNs, and transfer learning. You have experience building and optimizing models using architectures such as ResNet, VGG, or YOLO.
• Programming and Software Development: Proficiency in Python, C++, or a similar programming language, with experience writing production-level code. You can build, test, and deploy computer vision models efficiently.
• Model Deployment and Optimization: Experience deploying machine learning models into production environments using cloud platforms (AWS, GCP, Azure) and containerization tools like Docker. You understand how to optimize models for real-time performance.
• Data Handling and Preprocessing: Hands-on experience with data annotation, augmentation, and preprocessing. You know how to work with large datasets and prepare them for training deep learning models.

Educational Requirements:

• Bachelor’s or Master’s degree in Computer Science, AI, Electrical Engineering, or a related field. Equivalent experience in computer vision or machine learning is highly valued.
• Certifications or additional coursework in computer vision, deep learning, or AI are a plus.

Experience Requirements:

• 3+ years of experience in computer vision engineering, with a track record of building and deploying computer vision models in real-world applications.
• Proven experience with image and video analysis, and hands-on experience developing deep learning models for vision-related tasks.
• Experience working with cloud-based computer vision services (AWS Rekognition, Google Vision API, Azure Computer Vision) is highly desirable.