Machine Learning Engineer Intern - Paris - January 2027

Pernod Ricard

Paris

Hybride

EUR 16 000 - 17 000

Plein temps

Il y a 2 jours
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Avantages offerts par ce poste

Company restaurant
Work from home up to 2 days/week
Coursera training access
Employee events & networking

Résumé du poste

Pernod Ricard is seeking a Machine Learning / MLOps Intern for a 6-month program starting July 2026. Based in our central Paris office (Saint Lazare), you will scale data science models and collaborate with a team to deploy scalable pipelines using best MLOps practices.

You are pursuing a BS/MS in CS/Engineering or quantitative sciences, with experience in Python, ML, and CI/CD. Fluency in English is required; French is a plus. Remote work up to 2 days a week is offered.

Qualifications

  • Pursuing BS/MS in Computer Science, Computer Engineering or quantitative sciences.
  • PhD is a plus.

Responsabilités

  • Contribute to MLOps process implementation to maintain ML models in production.

Connaissances

Python
CI/CD
Machine Learning
English proficiency
Distributed systems
French familiarity

Formation

BS/MS in CS/Computer Engineering or quantitative sciences
PhD a plus

Outils

Docker
Azure ML Studio
MLFlow
Kubeflow

Description du poste

Want to join the world's No. 2 in wines & spirits and work among convivial teams? Pernod Ricard is looking for a Machine Learning /MLOps Intern for 6 months from July 2026. You will be based at the Island, our office in central Paris (Saint Lazare Train Station, Paris 8).

In this role, you’ll have the chance to roll up your sleeves to scale our data science models. Successful candidates areintellectually curious builderswho are biased towardaction, scrappy, and communicative.

Your key missions:
  • You will contribute to the MLOps process implementation to maintain ML models in production

  • You will collaborate with the team to deploy or improve scalable and efficient pipelines using best MLOps practices

  • You will collaborate with the team on production quality service development with Unit & Integration testing, CI/CD & devOps for these services

  • You will maintain and contribute to the API serving our web application

  • You will identify new opportunities to improve go-to-production, MLOps processes and improve end-to-end experiences

  • You will applysoftware development practices and standardsto enhance and ensure the code quality of our applications

  • You will maintain an active role in every part of thesoftware development life cycle

  • You will actively contribute toData platform community

Qualifications and experience:
  • You are pursuing a BS/MS in Computer Science, Computer Engineering, or quantitative science fields, a PhD is a plus

  • You have implementation experience with high-level languages, such as Python, Scala, Java, C/C++

  • You have experience in building or improving CI/CD pipelinesand in machine learning

  • You have knowledge and experience with container tools such as Docker

  • You are familiar with MLOps frameworks (Azure ML Studio, MLFlow, Kubeflow…)

  • You are fluent in English, speaking French is a plus

Please note: to apply, you must be able to complete 6 months of internship and have an agreement issued by your school/university.

And you’ll benefit from these advantages:
  • Gross salary from 1410€ (Bac +4 and gap year) to 1550€ (Bac +5) per month

  • Possibility to work from home up to 2 days a week.

  • Company restaurant

  • Unlimited access to the Coursera training platform to enhance your experience.

  • Employee events (Masterclasses, conferences, etc.)

All in a friendly, supportive environment that will help you to progress and build a solid professional network: 90.4% of our interns and alternates recommend us as an employer (Happy Trainees 2024)!

Pernod Ricard is committed to diversity and inclusion:

we are a disability-friendly company, one of France's Top 10 companies for gender equality, and we work with associations to promote social inclusion. Our recruitment methods focus on competencies to ensure equal opportunities.

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