Image and Video Processing Expert with modern AI tools
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Summary
* Seeking an Image and Video Processing Engineer to enhance a digital platform that captures images from park or venue cameras and kiosks.
* This role, which can be part-time, involves designing and developing image processing algorithms and AI models to deliver high-quality, automated photo editing and printing solutions. We use Ffmpeg and want to move to GPU processing.
* Requires 3 years with image processing and generation (modern AI-based services for image generation and enhancement)
* ML and LLMs for images and video processing.
About Project
Powered by patented technology, our platform combines advanced association methods with an AI learning engine to enable real-time media streaming at scale, without compromising guest privacy.
We are seeking a skilled Image Processing Engineer to enhance our photo kiosk infrastructure using modern AI tools. This role, which can be part-time, involves designing and developing image processing algorithms and AI models to deliver high-quality, automated photo editing and printing solutions, while also providing expertise to integrate advanced AI capabilities into our media processing pipelines. The candidate will integrate solutions with AWS/GCP cloud environments and collaborate with cross-functional teams to ensure seamless user experiences.
Key Responsibilities
- Algorithm Development: Design and implement image processing algorithms for tasks like image enhancement, color correction, and object detection.
- AI/ML Integration: Develop and deploy machine learning models (e.g., facial recognition, background removal) using frameworks like TensorFlow, PyTorch, or OpenCV.
- AI Consulting: Provide expertise to integrate advanced AI capabilities (e.g., generative AI, computer vision) into existing media processing pipelines.
- Cloud Integration: Integrate solutions with AWS/GCP environments, using services like S3 for storage or Lambda for serverless processing.
- Performance Optimization: Optimize algorithms for real-time performance on photo kiosk hardware and cloud systems.
- System Integration: Ensure AI and image processing solutions are compatible with kiosk software and user interfaces.
- Collaboration: Work with software engineers, UI/UX designers, and hardware teams to deliver intuitive kiosk functionalities.
- Testing & Validation: Test image quality and system performance under various conditions (e.g., lighting, resolution).
- Model Maintenance: Update AI models to support new features or customer requirements.
- Compliance: Ensure compliance with data privacy regulations (e.g., GDPR) for user images.
Qualifications
An Image Processing Engineer specializing in modern AI tools needs expertise in both traditional image processing techniques and cutting-edge machine learning algorithms, particularly deep learning. Key skills include experience with FFMPEG, GPU image processing, proficiency in Python, experience with deep learning frameworks such as TensorFlow or PyTorch, and a strong understanding of computer vision concepts, including image analysis, object detection, and image segmentation.
Education: Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field.
Experience:
- 3+ years in image processing, computer vision, or AI/ML development.
- Proficiency in Python, C++, or Java.
- Experience with FFMPEG
- Experience with AI frameworks (TensorFlow, PyTorch) and image processing libraries (OpenCV, PIL).
- Familiarity with AWS/GCP services (e.g., S3, Lambda) is a plus.
- GPU image/video processing (CUDA)
Must Have Skills:
- Strong knowledge of image processing techniques (e.g., filtering, segmentation).
- Expertise in deep learning for computer vision (e.g., Convolutional Neural Networks - CNNs).
- Ability to provide strategic AI consulting for media processing pipelines.
- Ability to optimize algorithms for hardware and cloud performance.
- Strong problem-solving and teamwork skills.
AI-Specific Skills:
- Convolutional Neural Networks (CNNs): CNNs are widely used for image processing tasks. Understanding their architecture and how to train them is essential.
- Transfer Learning: Leveraging pre-trained models on large datasets to accelerate development and improve performance.
- Image Augmentation: Techniques for artificially increasing the size and diversity of training datasets.
- Object Detection and Segmentation: Algorithms for identifying and outlining objects within images.
- Generative AI: Exploring techniques like Generative Adversarial Networks (GANs) for image generation and manipulation.
- Prompt Engineering: For applications utilizing large language models and image generation.
- Model Deployment: Deploying models to production environments and managing their lifecycle.
- Ethical AI: Understanding ethical considerations related to AI, especially concerning data privacy and bias.
Preferred:
- Familiarity with cloud-based AI services.
- Experience with photo kiosk systems or consumer-facing applications.
- Knowledge of UI/UX for photo editing interfaces.