WebRTC, LiveKit, Live Streaming, FFmpeg, Video Management System for IP cameras and Computer Vision

We are building a scalable video infrastructure platform for a sports technology solution and are looking for an experienced backend/video streaming engineer to design and implement the system. Cameras installed at sports venues continuously stream live video to a centralized relay server, which distributes each feed in multiple formats to support different downstream applications. These include RTSP streams for integration with a third-party AI analysis platform through LiveKit, low-latency live viewing for mobile devices using HLS or WebRTC, and automatic recording with replay footage securely stored in cloud storage. The platform also incorporates real-time computer vision powered by YOLO for object detection and recognition. Detection results are processed to generate intelligent alerts and notifications that are delivered instantly via Telegram. The ideal candidate should have strong experience with real-time video streaming technologies and media servers, including RTSP, RTP, WebRTC, HLS, FFmpeg, GStreamer, LiveKit, MediaMTX (or similar streaming servers), and cloud-based video storage solutions. Experience building scalable video pipelines, low-latency streaming systems, and distributed backend services is highly desirable. In addition, candidates should have practical experience with computer vision frameworks such as YOLO, OpenCV, TensorFlow, or PyTorch, including integrating object detection models into live video pipelines. Familiarity with REST APIs, WebSockets, Docker, Kubernetes, Linux, GPU acceleration (CUDA/TensorRT), and messaging or notification services such as Telegram Bot API is a significant advantage. Experience designing systems that can reliably scale from a handful of cameras to hundreds of concurrent live streams while maintaining high availability and low latency is highly preferred. Please apply with "VMS" at the beginning of your application.