Job information

Duties and Responsibilities 

• Develop, integrate, and deploy computer vision and machine learning applications for real-world use cases. 
• Implement efficient, reliable, and maintainable software using C++ and Python.
• Work on computer vision tasks such as object detection, object tracking, pose estimation, image classification, segmentation, and video analytics. 
• Review and cross-check existing source code, algorithms, and processing pipelines to understand their logic, data flow, dependencies, and expected behavior. 
• Validate end-to-end workflows, including data preparation, model inference, post-processing, application integration, and result reporting. 
• Design and conduct experiments to evaluate model accuracy, robustness, computational efficiency, and reliability. 
• Define appropriate evaluation protocols and metrics, including precision, recall, F1-score, detection accuracy, tracking accuracy, false-positive rate, false-negative rate, and inference speed. 
• Develop test cases and perform functional, integration, performance, and regression testing of AI models and computer vision applications. 
• Analyze model errors, identify failure cases, and collaborate with researchers and developers to improve system performance. 
• Prepare datasets, annotations, experimental configurations, evaluation reports, and technical documentation. 
• Optimize models and processing pipelines for deployment on servers, edge devices, and embedded computing platforms. 
• Collaborate with researchers, software engineers, data scientists, and domain experts to translate research prototypes into reliable applications. 
• Keep up to date with developments in computer vision, machine learning, Agentic AI, and related AI technologies.

Qualifications and Skills 

• Master’s or Ph.D. degree in Computer Science, Computer Engineering, Artificial Intelligence, Data Science, or a related field. 
• At least three years of hands-on experience in computer vision, machine learning, AI software development, or a related area. 
• Proficiency in C++ and Python. 
• Strong understanding of computer vision and machine learning concepts, algorithms, data structures, and software development principles. 
• Practical experience with computer vision and machine learning libraries or frameworks such as PyTorch, TensorFlow, NVIDIA DeepStream, scikit-learn, OpenCV, etc. 
• Experience in developing or integrating applications involving images, video streams, cameras, or vision-based AI models. 
• Ability to read and understand existing codebases, cross-check implementation logic, trace end-to-end workflows, and identify software- or model-related issues.
• Experience in evaluating machine learning models using appropriate datasets, baselines, experimental protocols, and performance metrics. 
• Familiarity with software testing, debugging, version control, and technical documentation. 
• Experience in deploying AI models in production or edge-computing environments is an advantage. 
• Experience with GPU acceleration, CUDA, TensorRT, ONNX, NVIDIA DeepStream, or NVIDIA Jetson platforms is an advantage. 
• Knowledge or practical experience in Agentic AI, Retrieval-Augmented Generation (RAG), and Model Context Protocol (MCP) is a plus. 
• Strong analytical and problem-solving abilities, with careful attention to accuracy, reliability, and implementation details. 
• Good communication and teamwork skills, including the ability to explain technical