28033 - MLOps Technician

Description

Job Summary: Design, automate, and maintain infrastructure for the development and deployment of AI solutions, collaborating with development and data science teams to optimize DevOps and MLOps processes. Key Highlights: 1. IA infrastructure design and automation 2. IaC and CI/CD implementation with Terraform, Ansible, and Jenkins 3. Collaboration with development and data science teams A leading engineering and consulting firm in sustainable mobility and digital transformation INCO has spent 55 years designing comprehensive, innovative, and technology-driven solutions that have advanced a new model of more sustainable and safer mobility. These solutions directly contribute to improving the quality of life for millions of people. With a multidisciplinary team of over 6,000 professionals, the company operates across all continents, deploying its expertise and capability to tackle technically complex projects through specialized knowledge and application of the most advanced and cutting-edge technologies. Responsibilities Design, automate, and maintain the infrastructure required for developing, training, and deploying Artificial Intelligence solutions. Implement and manage Infrastructure as Code (IaC) using tools such as Terraform and Ansible, and design and operate Continuous Integration and Continuous Deployment (CI/CD) pipelines using tools such as Jenkins. Collaborate with development and data science teams to facilitate production deployment of AI models, ensuring reproducible, scalable, and secure environments. Monitor platform performance, optimize deployment processes, and contribute to continuous improvement of DevOps and MLOps practices within the organization. Requirements Experience in: Designing, automating, and operating infrastructure and pipelines in cloud environments applying DevOps and MLOps principles. Participation in building CI/CD pipelines for automated deployment of applications and AI models, as well as managing Infrastructure as Code (Terraform) and configuration automation (Ansible). Experience working with containers and orchestration, integrating continuous integration tools such as Jenkins, and operating systems in production environments, including monitoring, incident management, and deployment optimization. Familiarity with the Machine Learning model lifecycle in production, collaborating with development and data teams to ensure reproducible, scalable, and reliable deployments. // Years of experience in the position: > 2 years // Qualifications: Computer Science – Vocational Training – Computer Science and Networks, Computer Science – Bachelor’s Degree in Computer Engineering, Computer Science – Bachelor’s Degree in Engineering and Data Systems, Computer Science – Computer Engineering // Offered Offer