Responsibilities Work directly with the ML Engineers team to put Machine Learning models into production using and creating ML pipelines Implement DevOps SRE metrics Administration and monitoring of team platforms Responsible for the infrastructure and its proper functioning Operation and monitoring of our applications To be the main promoter of practices and operationalization of the necessary pieces to serve our models and put them to interact with the rest of the company in a real environment and highly scalable Promote a technical culture driving data products with DevOps, SRE and MLOps Requirements At least 3 year of experience in work environments as Software Engineer, DevOps Engineer, SRE or ML Engineer Experience with Python Experience working in UNIX/LINUX environments Experience creating CI/CD pipelines Experience working with Docker Deploying, managing, and scaling AI models and applications in production environments using DevOps principles Experience in GIT Good spoken English Experience working with cloud services, preferably GCP Excellent communication skills and collaborative work Nice to have Experience in Kubernetes Experience designing and implementing automated tests Experience in observability, monitoring Understanding of the complete life flow of Machine Learning models Experience in infrastructure as code, desirable Terraform Experience with Jenkins Agility to visualize possible improvements, problems, and solutions in Architecture Experience with monitoring tools such as Grafana, Datadog, Nagios Useful links