Video Knowledge Engineer

Job Description: Video Knowledge Engineer Are you passionate about Computer Vision, Gen AI, and ready to dive into a thrilling project that blends cutting-edge technology with the vibrant world of entertainment? Were seeking a skilled Computer Vision Knowledge Engineer to participate in building an AI platform that revolutionizes how we create, curate, and deliver short-form content. You'll work alongside product, engineering, AI, and machine learning experts to build real-world AI systems that shape the future of entertainment. This role is ideal for someone who thrives at the intersection of Gen AI, machine learning, and software engineering, with a passion for building innovative AI products. Responsibilities: Coordinate with internal teams and vendors to identify and implement optimal metadata and video knowledge extraction solutions for the AI platform, enabling effective creation, curation, and delivery of short-form content. Collaborate with internal stakeholders and external vendors to evaluate, utilize, and enhance computer vision tools and libraries, ensuring robust video content analysis and metadata extraction. Work closely with the team, partners and vendors to design and implement computer vision algorithms that extract actionable insights and high-quality metadata from video content. Partner with DevOps and engineering teams to integrate computer vision models and metadata extraction solutions into scalable, containerized environments using Docker and Kubernetes. Engage with data scientists, machine learning engineers, DevOps, and vendors to deliver cohesive, scalable solutions for video knowledge extraction and metadata optimization. Qualifications/Requirements: Proficiency in Python . Solid experience with development and deployment tools, including containerization ( Docker , Kubernetes ) and workflow orchestration ( Temporal or similar). Familiarity with video analysis tools and libraries such as OpenCV , FFmpeg , or similar. Experience in computer vision algorithms , preferably with broad familiarity and use of existing tools and libraries (e.g., OpenCV , PySceneDetect ) for building CV applications. Expertise in multiple areas listed below: Object and people detection ( must ) Facial recognition ( must ) Image and video segmentation Activity recognition Video summarization Video identification Video behavior analysis Preferred Qualifications: Practical knowledge of artificial neural networks for computer vision, especially in Convolutional Neural Networks (CNNs) . Exposure to deep learning frameworks such as TensorFlow or PyTorch . Expertise in any additional CV areas listed below: Edge detection Style detection Gesture recognition Video tracking Object co-segmentation