6-Month MLOps ML Engineer Intern – Paris

Pernod-Ricard; Paris; On-site; EUR 16,000 - 17,000; Full time

Benefits offered by this job

Restaurant access

Coursera training access

Central Paris office

Work from home up to 2 days a week

Job summary

Pernod Ricard in central Paris is seeking a Machine Learning / MLOps Intern for 6 months starting July 2026. You will be based at the Island office near Saint Lazare, joining welcoming teams to scale data science models and push them into production.

You will work on MLOps processes, deploy scalable pipelines, contribute to API serving, CI/CD, and improve end-to-end production workflows, while applying software development practices and maintaining code quality.

Qualifications

• Pursuing BS/MS in Computer Science, Computer Engineering or quantitative sciences (PhD a plus).
• Experience with high-level languages: Python, Scala, Java, C/C++.
• Experience building or improving CI/CD pipelines and in machine learning.
• Knowledge of container tools such as Docker and MLOps frameworks (Azure ML Studio, MLFlow, Kubeflow).
• Fluent in English; French a plus.

Responsibilities

• Contribute to MLOps process implementation to maintain ML models in production.
• Collaborate to deploy or improve scalable and efficient pipelines using best MLOps practices.
• Develop production-quality services with unit and integration testing, CI/CD & DevOps for these services.
• Maintain and contribute to API serving our web application.
• Identify opportunities to improve go-to-production, MLOps processes and end-to-end experiences.
• Apply software development practices to enhance code quality and participate in the full SDLC.
• Engage in Data Platform community activities.

Tools

Python

Scala

Java

C/C++

Docker

CI/CD

DevOps

MLOps

Job description

Pernod Ricard in central Paris is seeking a Machine Learning / MLOps Intern for 6 months starting July 2026. You will be based at the Island office near Saint Lazare, joining welcoming teams to scale data science models and push them into production.

You will work on MLOps processes, deploy scalable pipelines, contribute to API serving, CI/CD, and improve end-to-end production workflows, while applying software development practices and maintaining code quality.