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

KION Group; Barcelona; Híbrido; EUR 65.000 - 90.000; Tempo integral

Benefícios oferecidos por esta vaga

Private health insurance

Food allowance

Hybrid work model

Flexible working hours

International projects

Resumo da vaga

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.

Qualificações

• 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

Conhecimentos

Python

AI/ML end-to-end

Kubernetes

MLOps

Langfuse

Formação

Computer science / mathematics / physics degree

Ferramentas

MLflow

Azure

GCP

Kubernetes

Descrição da vaga

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.