COMPUTER VISION / AI SYSTEMS ARCHITECT

As a Computer Vision / AI Systems Architect, your main focus will be on designing, developing, and deploying advanced artificial intelligence and computer vision solutions. You will be responsible for architecting scalable AI systems, creating machine learning models for visual data analysis, and integrating AI technologies into real-world applications. Collaboration with teams to ensure the delivery of high-performance AI-driven solutions is a key aspect of your role. Key Responsibilities - Design and develop end-to-end AI and computer vision system architectures. - Build and optimize machine learning and deep learning models for image and video analysis. - Develop algorithms for object detection, image classification, segmentation, tracking, and pattern recognition. - Lead the deployment of AI models into production environments. - Collaborate with engineering teams to integrate AI models into applications, platforms, and embedded systems. - Ensure scalability, efficiency, and reliability of AI solutions. - Evaluate and adopt emerging AI frameworks, tools, and technologies. - Conduct research and experimentation to improve model performance and accuracy. - Provide technical leadership and mentorship to AI engineers and developers. - Maintain documentation of system architecture, processes, and technical specifications. Required Qualifications - Masters in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, IOT, or related field. - Strong experience in deep learning frameworks such as TensorFlow, PyTorch, or Keras. - Proficiency in Python, C++, or similar programming languages. - Experience with computer vision libraries such as OpenCV. - Knowledge of CNNs, neural networks, and advanced AI algorithms. - Experience in deploying AI models in cloud environments (AWS, Azure, GCP). - Strong understanding of data structures, algorithms, and software architecture. Preferred Skills - Experience in real-time video analytics and edge AI systems. - Knowledge of MLOps, model optimization, and GPU acceleration. - Familiarity with IoT systems, robotics, or autonomous systems. - Strong analytical, problem-solving, and communication skills. Key Competencies - AI system architecture design - Computer vision algorithm development - Machine learning model optimization - Technical leadership and innovation - Cross-functional collaboration As a Computer Vision / AI Systems Architect, your main focus will be on designing, developing, and deploying advanced artificial intelligence and computer vision solutions. You will be responsible for architecting scalable AI systems, creating machine learning models for visual data analysis, and integrating AI technologies into real-world applications. Collaboration with teams to ensure the delivery of high-performance AI-driven solutions is a key aspect of your role. Key Responsibilities - Design and develop end-to-end AI and computer vision system architectures. - Build and optimize machine learning and deep learning models for image and video analysis. - Develop algorithms for object detection, image classification, segmentation, tracking, and pattern recognition. - Lead the deployment of AI models into production environments. - Collaborate with engineering teams to integrate AI models into applications, platforms, and embedded systems. - Ensure scalability, efficiency, and reliability of AI solutions. - Evaluate and adopt emerging AI frameworks, tools, and technologies. - Conduct research and experimentation to improve model performance and accuracy. - Provide technical leadership and mentorship to AI engineers and developers. - Maintain documentation of system architecture, processes, and technical specifications. Required Qualifications - Masters in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, IOT, or related field. - Strong experience in deep learning frameworks such as TensorFlow, PyTorch, or Keras. - Proficiency in Python, C++, or similar programming languages. - Experience with computer vision libraries such as OpenCV. - Knowledge of CNNs, neural networks, and advanced AI algorithms. - Experience in deploying AI models in cloud environments (AWS, Azure, GCP). - Strong understanding of data structures, algorithms, and software architecture. Preferred Skills - Experience in real-time video analytics and edge AI systems. - Knowledge of MLOps, model optimization, and GPU acceleration. - Familiarity with IoT systems, robotics, or autonomous systems. - Strong analytical, problem-solving, and communication skills. Key Competencies - AI system architecture design - Computer vision algorithm development - Machine learning model optimization - Technical leadership and innovation - Cross-functional collaboration