**DESCRIPTION** Responsible for the development and implementation of Artificial Intelligence-based solutions applied to X-ray equipment image analysis. Performs data collection and processing, training of AI models (including computer vision and NLP), integration with production systems via APIs and pipelines, and maintenance of deployment environments. Collaborates with multidisciplinary teams to ensure the effectiveness and scalability of solutions, following MLOps and versioning best practices. Requires solid technical background and experience with AI frameworks, containerization, and model lifecycle management. **RESPONSIBILITIES** * Collect, organize, and process large volumes of structured and unstructured data. * Develop, train, and validate AI models for tasks such as image classification, segmentation, anomaly detection, and synthetic image generation. * Apply Natural Language Processing (NLP) techniques and large language models (LLMs) to analyze reports, assessments, and technical documentation. * Design and maintain inference APIs and pipelines for model integration into production environments. * Implement containerization practices using Docker and deploy models into production environments. * Collaborate with engineering, software, and product teams to ensure efficient integration of AI solutions. * Document processes, maintain code versioning, and participate in technical and code reviews. Adopt MLOps best practices, including monitoring, reprocessing, and continuous model updates. **PREFERRED QUALIFICATIONS** **EDUCATION** Bachelor's degree completed in Computer Science, Computer Engineering, Information Systems, or related fields \+ Postgraduate or master's degree in Artificial Intelligence, Data Science, Machine Learning, or related areas. **SPECIFIC KNOWLEDGE** * Proficiency in Python and experience with AI frameworks such as PyTorch and TensorFlow. * Knowledge of libraries like OpenCV and image processing techniques, including classification, segmentation, and object detection. * Experience with Flask, Docker, CI/CD, and model monitoring tools such as MLflow. * Use of Git, GitHub/GitLab, dataset and model versioning. * Competence in developing, integrating, and maintaining data and model pipelines in production environments.