Take your career to the next level with Amaris Consulting as an MLOps Engineer. Become part of an international team, thrive in a global group with €800M turnover and 1,000+ clients worldwide, and an agile environment by planning the kickoff and follow up on projects. Join Amaris Consulting, where you can develop your potential and make a difference within the company.

WHAT WOULD YOU NEED? ✍️

• 3 to 5 years of experience designing and implementing MLOps solutions.
• Strong experience with AWS , including infrastructure implementation, automation, and security management.
• Hands-on experience with Terraform and Infrastructure as Code (IaC).
• Solid experience with Kubernetes , including deployment, cluster management, and automation of containerized applications.
• Knowledge of MLOps frameworks and tools such as MLflow , Kubeflow, TFX, and Apache Airflow .
• Experience working with Machine Learning workflows and production environments.
• Strong programming skills in Python .
• Experience with CI/CD practices, version control systems, and monitoring tools.
• Strong problem-solving and analytical skills.
• Excellent communication and collaboration abilities.
• Professional working proficiency in English.
• Good command of Spanish .
• Based in Barcelona or willing to work in a 50% hybrid model .

WHAT WILL YOU DO?

• Design, implement, and maintain scalable MLOps platforms and solutions.
• Build and manage cloud infrastructure on AWS following best practices for security, scalability, and automation.
• Develop and maintain Infrastructure as Code using Terraform .
• Deploy, manage, and optimize Kubernetes clusters and containerized workloads.
• Implement and improve Machine Learning workflows using tools such as MLflow , Kubeflow, TFX, and Apache Airflow .
• Automate deployment, monitoring, and lifecycle management of machine learning models.
• Collaborate with Data Scientists, Data Engineers, and Software Engineers to streamline ML development and production processes.
• Monitor platform performance and ensure reliability, scalability, and operational excellence.
• Contribute to the definition of technical standards, best practices, and continuous improvement initiatives.
• Support the adoption of MLOps methodologies across different teams and projects.