Senior ML/AI Engineer with GCP

Location: Montevideo, Uruguay; Remote.AR, Remote.LATAM-country; Remote.CO, Colombia; Rosario, Remote.LATAM-country We are looking for an ML/AI Engineer with solid knowledge of cloud technologies, Infrastructure as Code (IaC), CI/CD practices, and software engineering best practices, focused on designing, building, and operationalizing Generative AI based solutions. You will join a strategic initiative within the Emantto domain, contributing to the acceleration of the organization's AI portfolio across multiple business areas. This role combines ML/AI engineering with strong software engineering, cloud, and infrastructure expertise. You will build and operate Generative AI driven data products, support domain teams, and act as a facilitator for the Data & AI Platform. Develop and deliver data products and AI/Generative AI solutions within domain teams. Act as a facilitator of the Data & AI Platform, enabling adoption and accelerating delivery across teams. Build, deploy, and operate Generative AI and Machine Learning models in scalable, production ready environments. Manage infrastructure related aspects of environments and AI/ML products, including observability, performance, and reliability. Contribute to CI/CD pipelines, Infrastructure as Code practices, and platform automation initiatives. Collaborate with cross functional teams, including Software Engineering, Data Engineering, MLOps, and DevOps teams, to maintain high engineering standards. Support experimentation frameworks and internal tools for Generative AI model development and evaluation. Experience working with Google Cloud Platform (GCP). Experience with Terraform or other Infrastructure as Code tools. Strong proficiency in Python. Experience in backend engineering, including APIs and services, as well as Generative AI, Machine Learning, or MLOps technologies such as Airflow, MLflow, pipelines, and monitoring tools. Solid understanding of CI/CD practices, containerization using Docker, and software engineering best practices. Familiarity with model deployment, model serving, and operating Machine Learning and AI systems in production environments. Experience with observability, incident response, or platform operations.