ML Platform Engineer: MLOps, Data Pipelines & Cloud

FR10088-ALGLOBSY Air Liquide IT; Laval; Sur place; EUR 70 000 - 105 000; Plein temps

Résumé du poste

Air Liquide, through Digital & IT Data & AI Applications, seeks an experienced Machine Learning Engineer to own ML lifecycle execution across development to production. You will collaborate with data scientists, engineers and product teams to deploy scalable ML solutions and continuously improve platforms.

You will deploy robust ML pipelines, optimize performance and contribute to ML Ops initiatives within a global engineering chapter, while mentoring colleagues and participating in Agile

Qualifications

• 5+ years in MLOps/DevOps or cloud infrastructure roles.
• Proficient Python, including refactoring existing code and best practices.
• Experience with CI/CD, containerization, IaC and Git in ML contexts.
• Cloud platforms (AWS, GCP) with focus on scalable ML deployment.
• Experience with ML tools like Poetry, uv, MLflow, Airflow, SageMaker, Podman, Docker.
• Proficient in English and French, both written and spoken.

Responsabilités

• Provide complete ML environments for Data Science teams following Air Liquide standards.
• Collaborate with data engineers, software developers and product owners to integrate ML solutions.
• Deploy and manage robust ML pipelines for CI/CD, monitoring and retraining.
• Profile, debug and optimize model and data pipelines for production performance.
• Work with ML Ops platform teams to translate product needs into feature requests.
• Mentor and train other engineers and share knowledge across the team.
• Participate in Agile ceremonies and team rituals to meet sprint goals.

Connaissances

Python

MLOps

DevOps

English & French

Communication

Collaboration

Outils

AWS

GCP

Docker

Podman

Airflow

MLflow

SageMaker

Description du poste

Air Liquide, through Digital & IT Data & AI Applications, seeks an experienced Machine Learning Engineer to own ML lifecycle execution across development to production. You will collaborate with data scientists, engineers and product teams to deploy scalable ML solutions and continuously improve platforms.

You will deploy robust ML pipelines, optimize performance and contribute to ML Ops initiatives within a global engineering chapter, while mentoring colleagues and participating in Agile