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Computer Vision Engineer

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Hire Computer Vision Engineer
for up to 60% less

Average Salary in the UK £7083.33/month
Omni Talent

£3010.42/month

Average omni talent Salary
57.50% Potential Savings
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Computer Vision Engineer Job Description

Tasks:

  • Algorithm Development & Implementation:
    • Design and implement computer vision algorithms for object detection, image segmentation, tracking, pose estimation, OCR, and more.
    • Optimise and deploy deep learning models for real-time image and video processing tasks.
    • Utilise frameworks such as OpenCV, TensorFlow, PyTorch, and NVIDIA DeepStream.
    • Develop algorithms to enhance image quality, denoise, and improve feature extraction.
  • Data Processing & Model Training:
    • Prepare, preprocess, and augment large datasets for model training and validation.
    • Train and fine-tune deep learning models for various applications such as facial recognition, image classification, and video analytics.
    • Implement transfer learning and fine-tuning techniques for faster model convergence.
    • Work with synthetic data and data labelling tools to improve model performance.
  • Optimisation & Deployment:
    • Optimise model inference using techniques such as quantisation, pruning, and knowledge distillation.
    • Deploy models to edge devices, cloud, and embedded systems for real-time applications.
    • Work with deployment frameworks such as TensorRT, ONNX, and OpenVINO for inference acceleration.
    • Develop APIs and pipelines to integrate vision models with applications.
  • Research & Innovation:
    • Stay up-to-date with the latest advancements in computer vision, machine learning, and deep learning.
    • Experiment with cutting-edge technologies such as generative models (GANs), multi-view vision, and 3D reconstruction.
    • Participate in developing research papers and patents for innovative computer vision solutions.
  • Testing & Evaluation:
    • Conduct rigorous testing and evaluation of vision models using appropriate metrics such as IoU, mAP, F1-score, and latency.
    • Identify and resolve model biases and limitations by improving dataset diversity and model robustness.
    • Perform A/B testing and analyze results to improve production models.
  • Collaboration & Documentation:
    • Work closely with software engineers, data scientists, and hardware teams to integrate computer vision solutions.
    • Document technical specifications, methodologies, and findings for internal and external stakeholders.
    • Participate in code reviews and contribute to the continuous improvement of the team’s best practices.

Qualifications:

  • Strong proficiency in Python and experience with deep learning frameworks such as TensorFlow, PyTorch, and Keras.
  • Solid understanding of image processing techniques (filters, transformations, feature extraction).
  • Experience with computer vision libraries such as OpenCV, DLIB, and Scikit-Image.
  • Familiarity with cloud-based solutions (AWS, Azure, Google Cloud) for AI model deployment.
  • Experience in deploying vision models to edge devices (Jetson, Raspberry Pi) and embedded platforms.
  • Strong grasp of deep learning concepts including CNNs, RNNs, and attention mechanisms.
  • Hands-on experience with GPU acceleration (CUDA, cuDNN) and optimisation techniques.
  • Knowledge of computer vision evaluation metrics and performance tuning.
  • Strong problem-solving skills and ability to work in a fast-paced environment.

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