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Home/Jobs/SDE-1 (Computer Vision Engineer)
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Master-Works

SDE-1 (Computer Vision Engineer)

🇸🇦 Riyadh, Saudi Arabia🏢 On-site
Computer VisionOpenCVPythonKerasTensorFlowPyTorchDockerKubernetes
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Responsibilities

• Solve real-world Computer Vision problems using cutting-edge techniques.
• Design and implement robust, industry-grade algorithms.
• Utilize OpenCV, Python, and deep learning frameworks for model training.
• Work with deep learning libraries such as Keras, TensorFlow, and PyTorch.
• Develop integrations with internal and external microservices.
• Implement deployment practices using Docker, Kubernetes, and model compression techniques.
• Research the latest technologies and develop proof of concepts (POCs).
• Build and train state-of-the-art deep learning models for:
• Object Detection (Mandatory)
• Segmentation
• Classification
• Object Tracking
• Visual Style Transfer
• Generative Adversarial Networks (GANs)
• Collaborate with researchers and engineers to develop and deploy Computer Vision solutions.
• Plan and execute Computer Vision research projects, defining scope, objectives, and deliverables.
• Provide specialized technical and scientific research support for ongoing and new AI initiatives.Requirements

Required Skills

• C++ (Mandatory)
• Python (Mandatory)
• Image Processing (Mandatory)
• Computer Vision (Mandatory)
• Deep Learning (Mandatory)
• Object Detection
• Machine Learning
• Pattern Recognition
• Artificial Intelligence (AI)
• Data Science
• Generative Adversarial Networks (GANs)
• Flask
• SQL

Requirements

  • •C++ (Mandatory)
  • •Python (Mandatory)
  • •Image Processing (Mandatory)
  • •Computer Vision (Mandatory)
  • •Deep Learning (Mandatory)
  • •Object Detection
  • •Machine Learning
  • •Pattern Recognition

Nice to Have

  • •AI
  • •Data Science
  • •Generative Adversarial Networks (GANs)
  • •Flask
  • •SQL

Responsibilities

  • •Solve real-world Computer Vision problems using cutting-edge techniques.
  • •Design and implement robust, industry-grade algorithms.
  • •Utilize OpenCV, Python, and deep learning frameworks for model training.
  • •Work with deep learning libraries such as Keras, TensorFlow, and PyTorch.
  • •Develop integrations with internal and external microservices.
  • •Implement deployment practices using Docker, Kubernetes, and model compression techniques.
  • •Research the latest technologies and develop proof of concepts (POCs).
  • •Build and train state-of-the-art deep learning models.

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