• Develop, train, validate and deploy Machine Learning and Artificial Intelligence models in production environments; • Work through the full data science project lifecycle, from data collection, cleaning and preparation to modeling, evaluation and deployment; • Use tools and frameworks such as Python, TensorFlow, PyTorch, scikit-learn, pandas and numpy to develop solutions; • Implement and monitor MLOps pipelines, ensuring model scalability, reproducibility and quality; • Work with cloud computing platforms (AWS, Azure or GCP) to provision and manage processing and storage resources; • Perform queries and analyses on relational and NoSQL databases; • Apply mathematical and statistical foundations (linear algebra, calculus, probability and optimization) when building robust solutions; • Ensure best practices for versioning, documentation and monitoring of models in production; • Collaborate with multidisciplinary teams to identify opportunities for AI/ML applications and turn business needs into data-driven solutions; • Contribute to the team’s technical growth by sharing knowledge and guiding best practices in data science.