AI Engineer - Computer Vision

## About the role We are seeking a skilled and passionate AI Video Analytics Engineer with hands-on experience in edge computing and embedded systems. The ideal candidate will be responsible for designing and deploying AI-powered video analytics solutions on edge devices, with a strong understanding of real-time CCTV data streaming and RTSP protocols. You will play a key role in building scalable, intelligent systems for real-time video processing in bandwidth- and latency-sensitive environments. Only candidates with relevant experience should apply ## Responsibilities - Design, develop, and optimize AI video analytics algorithms for deployment on edge devices. - Set up, configure, and manage edge computing devices (e.g., NVIDIA Jetson, Raspberry Pi, etc.) for real-time video processing applications. - Develop embedded software and firmware for edge devices to support AI workloads. - Implement and maintain real-time CCTV data streaming solutions using RTSP and other relevant protocols. - Integrate video analytics pipelines with edge hardware for efficient performance and minimal latency. - Work closely with hardware teams, data scientists, and system architects to deliver robust and scalable edge AI solutions. - Optimize system performance and troubleshoot hardware/software issues in edge environments. - Stay current with industry trends and emerging technologies in edge AI and computer vision. ## Requirements - Bachelor's or Master's degree in Computer Science, Electrical/Electronics Engineering, or a related field. - Proven experience in embedded software development using C/C++/Python for edge devices. - Strong hands-on experience with edge computing platforms such as NVIDIA Jetson (Nano, Xavier), Raspberry Pi, or similar. - Expertise in handling video or image processing, with a solid understanding of CCTV systems and real-time data streaming. - In-depth knowledge of RTSP and related streaming protocols. - Familiarity with AI/ML frameworks such as TensorRT, OpenVINO, ONNX, or similar for edge inference. - Experience working with camera integration, video codecs, and real-time video or image processing. - Strong problem-solving skills and the ability to work independently in fast-paced environments. ## Nice to have - Experience with containerization (Docker) and deployment of microservices on edge devices. - Knowledge of cloud-edge integration and protocols like MQTT. - Familiarity with computer vision libraries like OpenCV, GStreamer, or DeepStream SDK. - Exposure to security protocols and best practices in video surveillance systems. ## What we offer - Work on cutting-edge AI and edge computing technologies. - Be part of a dynamic team solving real-world video analytics challenges. - Opportunity to grow in a high-impact and rapidly evolving tech domain. ## About the company [No content provided in the source text]