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 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. Experience deploying models in production, especially on GPU/edge devices. Interest in reinforcement learning, sports analytics, or real-time systems An undergraduate degree (Master's or PhD preferred) in Computer Science, Artificial Intelligence, or a related discipline is preferred. A strong academic background is a plus. This job was posted by Siddhartha Dutta from Tech At Play.
We are seeking a proactive Computer Vision Engineer who excels in dynamic environments and has a passion for developing practical AI systems. If you have a keen interest in working with video, visual data, cutting-edge ML models, and resolving impactful challenges, we are eager to connect with you. This position merges deep learning, computer vision, and edge AI, focusing on constructing scalable models and intelligent systems to drive our advanced sports technology platform. Your responsibilities will include designing, training, and refining deep learning models for real-time object detection, tracking, and video comprehension. You will be responsible for implementing and deploying AI models utilizing frameworks such as PyTorch, TensorFlow/Keras, and Transformers. Working with video and image datasets using tools like OpenCV, YOLO, NumPy, Pandas, and visualization tools like Matplotlib will be a key aspect of your role. Collaborating closely with data engineers and edge teams to deploy models on real-time streaming pipelines will also be part of your duties. Additionally, you will need to optimize inference performance for edge devices such as Jetson and T4, and manage video ingestion workflows. You will also be expected to rapidly prototype new concepts, perform A/B tests, and validate enhancements in real-world scenarios. Clear documentation of processes, effective communication of findings, and contributing to the expansion of our AI knowledge base are essential aspects of this role. To be successful in this position, you should possess a strong command of Python and have familiarity with C/C++. Experience with deep learning frameworks such as PyTorch, TensorFlow, and Keras is required. A solid understanding of YOLO, Transformers, or OpenCV for real-time visual AI is essential. Proficiency in data preprocessing, feature engineering, and model evaluation using NumPy, Pandas, etc., is also necessary. A good grasp of computer vision, convolutional neural networks (CNNs), and object detection techniques is expected. Exposure to video streaming workflows like GStreamer, FFmpeg, and RTSP will be advantageous. The ability to write clean, modular, and efficient code is crucial for this role. Experience in deploying models in production, particularly on GPU/edge devices, is highly valued. An interest in reinforcement learning, sports analytics, or real-time systems will be considered a plus. An undergraduate degree in Computer Science, Artificial Intelligence, or a related field is required, while a Master's or PhD is preferred. A strong academic background will be beneficial for this position.,