Ai Infrastructure Engineer

beBeeMlopps; Sevilla; Presencial; EUR 45.000 - 65.000; Jornada completa

Descripción de la vacante

A tech company in Sevilla is seeking an AI Infrastructure Engineer to support AI model deployment and optimization. Candidates should have strong Python scripting skills and experience with tools like Docker, Kubernetes, and MLflow. This role involves building infrastructure and managing CI/CD pipelines for deployed models. If you are passionate about MLOps and DevOps engineering, apply now.

Formación

• Strong experience in Python scripting.
• Proficiency with ML lifecycle tools such as MLflow and DVC.
• Familiarity with infrastructure as code and monitoring tools.

Responsabilidades

• Design, build, and maintain infrastructure for AI model deployment.
• Implement and manage Continuous Integration and Continuous Deployment pipelines.
• Develop strategies for versioning and monitoring deployed models.

Conocimientos

Python scripting

Machine learning model deployment

CI/CD pipelines

Docker

Kubernetes

MLflow

DVC

Herramientas

Docker

Kubernetes

Descripción del empleo

As a pivotal member of our team, we are seeking an expert in Artificial Intelligence and software deployment to fill the role of AI Infrastructure Engineer. This highly technical position involves supporting the development and deployment of machine learning models, ensuring seamless integration with existing systems and guaranteeing optimal performance.Key ResponsibilitiesDesign, build, and maintain infrastructure for AI model deployment, focusing on GPU-based solutions and optimizing deep learning model execution.Implement and manage Continuous Integration and Continuous Deployment (CI / CD) pipelines for model updates, collaborating closely with data scientists and engineers.Develop and implement strategies for versioning and monitoring deployed models, utilizing tools like Docker and Kubernetes.The ideal candidate will possess strong experience in Python scripting, as well as proficiency with ML lifecycle tools such as MLflow and DVC. Familiarity with infrastructure as code and monitoring tools is also essential. If you are passionate about MLOps and DevOps engineering and have the necessary skills, please submit your application or send your C.V.

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