Job
Description
We're looking for a hands-on Computer Vision Engineer who thrives in fast-moving environments and loves building real-world, production-grade AI systems. If you enjoy working with video, visual data, cutting-edge ML models, and solving high-impact problems, we want to talk to you. This role sits at the intersection of deep learning, computer vision, and edge AI — building scalable models and intelligent systems that power our next-generation sports tech platform. Key Responsibilities Design, train, and optimize deep learning models for real-time object detection, tracking, and video understanding. Implement and deploy AI models using frameworks like PyTorch, TensorFlow/Keras, and Transformers. Work with video and image datasets using OpenCV, YOLO, NumPy, Pandas, and visualization tools like Matplotlib. Collaborate with data engineers and edge teams to deploy models on real-time streaming pipelines. Optimize inference performance for edge devices (Jetson, T4, etc.) and handle video ingestion workflows. Prototype new ideas rapidly, conduct A/B tests, and validate improvements in real-world scenarios. Document processes, communicate findings clearly, and contribute to our growing AI knowledge base. Requirements Strong command of Python and familiarity with C/C++. Experience with one or more deep learning frameworks: PyTorch, TensorFlow, Keras. Solid foundation in YOLO, Transformers, or OpenCV for real-time visual AI. Understanding of data preprocessing, feature engineering, and model evaluation using NumPy, Pandas, etc. Good grasp of computer vision, convolutional neural networks (CNNs), and object detection techniques. Exposure to video streaming workflows (e.g., GStreamer, FFmpeg, RTSP). Ability to write clean, modular, and efficient code. A self-starter and builder at heart — comfortable with ambiguity and fast execution. Passionate about solving real-world problems using AI. Comfortable working in a startup culture: lean teams, rapid iteration, high ownership. A collaborative team player who’s curious, coachable, and open to feedback. Undergraduate degree (Master’s or PhD preferable) in Computer Science, Artificial Intelligence, or a related discipline is preferred. A strong academic background is a plus. Nice To Have Experience deploying models in production, especially on GPU/edge devices. Interest in reinforcement learning, sports analytics, or real-time systems. About Company: We are a young startup developing our own indigenous sports analytical platform called TECH AT PLAY. It is the ultimate analytics ecosystem for athletes, It automatically records, categorizes, and analyzes performance while live-streaming the game to sports fans across the globe, all from a single platform. Show more Show less
