SDE-1 (Computer Vision Engineer)
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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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60 seconds. $3.99 one-time.