Revolutionize your business with Smart IT Frame, a Global Innovative Digital solution & Service Provider
About the Company
Smart IT Frame, a leading IT solutions provider, offers innovative services in AI, machine learning, cybersecurity, and more. With a focus on diversity, equality, and inclusion, Smart IT Frame delivers cutting-edge solutions across various industries including finance, healthcare, telecommunications, and retail. With a global presence, the company is committed to fostering long-term partnerships built on trust and reliability.
About the Role
The Computer Vision Knowledge Engineer will contribute to the development of an AI platform that revolutionizes the creation, curation, and delivery of short-form content. This role is designed for someone passionate about blending generative AI, machine learning, and software engineering to create innovative products in the entertainment industry. As a key team member, you will work closely with cross-functional teams to design and implement advanced computer vision systems that enhance video content analysis and metadata extraction.
Responsibilities
- Collaborate with internal teams and external vendors to identify and implement metadata and video knowledge extraction solutions for AI-driven content creation.
- Enhance and evaluate computer vision tools and libraries to improve video content analysis and metadata extraction.
- Design and implement computer vision algorithms to extract actionable insights and high-quality metadata from video content.
- Work alongside DevOps and engineering teams to integrate computer vision models into scalable, containerized environments using Docker and Kubernetes.
- Partner with data scientists and machine learning engineers to deliver cohesive, scalable solutions for video knowledge extraction and optimization.
Required Skills
- Proficiency in Python and strong experience in development and deployment tools.
- Expertise in containerization (Docker, Kubernetes) and workflow orchestration (e.g., Temporal).
- Familiarity with video analysis tools and libraries such as OpenCV and FFmpeg.
- Experience with computer vision algorithms and tools (e.g., OpenCV, PySceneDetect).
- Strong knowledge of object and people detection, facial recognition, video segmentation, and activity recognition.
- Familiarity with video summarization, video identification, and video behavior analysis.
Preferred Qualifications
- Practical knowledge of artificial neural networks, especially Convolutional Neural Networks (CNNs).
- Exposure to deep learning frameworks like TensorFlow or PyTorch.
- Expertise in additional computer vision areas such as edge detection, style detection, gesture recognition, and video tracking.
- Experience with object co-segmentation in video processing.