AI-Driven Backend Engineer (MLOps & Cloud) – Hybrid

KION Group; Barcelona; Híbrido; EUR 65.000 - 90.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Private health insurance

Food allowance

Hybrid work model

Flexible working hours

International projects

Descripción de la vacante

Linde Material Handling in Barcelona is seeking a highly skilled AI/ML engineer to design, build, and deploy innovative data analytics solutions for internal and external customers. You will apply ML and generative AI models to end-to-end solutions and build MLOps on cloud platforms.

The role collaborates with data scientists, engineers, and IT to optimize AI deployments, monitor performance, and drive cost efficiency.

Formación

• 3+ years of professional experience in AI/ML/ analytics
• Completed studies in computer science, mathematics, physics or a comparable education
• Fluent in English and good communication skills
• Ability to design, implement and maintain AI-driven applications on cloud (Azure and GCP)

Responsabilidades

• Design, build and deploy innovative data analytics solutions for internal and external customers
• Apply machine learning and generative AI models for end-to-end solution building
• Build MLOps on cloud (Azure and GCP) and establish CI/CD pipelines
• AI model review, refactoring, containerization, deployment, versioning and monitoring
• AI model validation, testing, and integration
• Collaborate with data scientists, data engineers, business units, UX and IT teams

Conocimientos

Python

AI/ML end-to-end

Kubernetes

MLOps

Langfuse

Educación

Computer science / mathematics / physics degree

Herramientas

MLflow

Azure

GCP

Kubernetes

Descripción del empleo

Linde Material Handling in Barcelona is seeking a highly skilled AI/ML engineer to design, build, and deploy innovative data analytics solutions for internal and external customers. You will apply ML and generative AI models to end-to-end solutions and build MLOps on cloud platforms.

The role collaborates with data scientists, engineers, and IT to optimize AI deployments, monitor performance, and drive cost efficiency.