Alternance ML Engineer

About RTE International

RTE international is a consulting and engineering firm covering all aspects of electricity transmission. RTE international serves its clients worldwide to help them develop reliable, competitive electricity systems that meet the challenges of the energy transition.

As a subsidiary of RTE, Europe's largest electricity transmission network operator, RTE international offers tailor-made solutions to players in the electricity system sector in the development, operation, and maintenance of networks.

Since its creation in 2006, RTE international's experts, drawing on the know-how and skills developed over more than 70 years within RTE, have carried out more than 300 projects in over 50 countries on all continents. RTE international employs 70 people and nearly a hundred experts per year.

Benefits

• RTT
• Meal vouchers
• Health insurance
• Profit sharing
• Gift vouchers
• Phone
• 50% Transport subsidy

Missions

Context

We are evaluating the integration of OpenSTEF, an open-source framework for short-term energy forecasting (load, solar production, wind), developed by Alliander (the Dutch network operator) and hosted by LF Energy, the Linux Foundation's foundation dedicated to the energy transition.

The challenge: evaluate how to integrate and deploy this framework in an industrial context. We are at the exploration and design stage, which means you are arriving at the right time, when decisions are really being made.

Missions

1. Understand the market You will start by mapping existing energy forecasting solutions (licensed or open source) and precisely analyzing what OpenSTEF offers (or doesn't) compared to alternatives. This study will directly inform the project strategy.

2. Design the architecture You will study different ways to deploy OpenSTEF on an industrial scale: managed platforms like Databricks, cloud solutions, or an open-source stack with Docker + Kubernetes. You will propose a scalable, modular, and well-argued architecture.

3. Develop and validate You will set up an operational MLOps pipeline with MLflow, benchmark forecasting models, and validate the results on real data, specifically time series of solar production and grid load.

4. Contribute to an international open-source project You will be in regular contact with OpenSTEF developers at Alliander in the Netherlands. You will share our feedback, participate in community discussions, and follow the project's evolution to stay aligned with its developments.

What you will learn

This position is rare because it combines dimensions rarely seen together in an apprenticeship program:

• Design an end-to-end MLOps architecture for a concrete business case.
• Contribute to a recognized international open-source project (LF Energy).
• Work on modeling, deployment, and technological strategy.
• Communicate directly with international developers, in English.
• Understand the real challenges of energy forecasting in an industrial context.

Profile

Education

• Master's degree or engineering school (computer science, data science, or related field).
• We are looking for an apprenticeship of 2 to 3 years.

What you need to master

• Python (pandas, scikit-learn, XGBoost / LightGBM)
• Machine learning fundamentals: cross-validation, metrics, overfitting
• Linux and command line, comfortable in the terminal
• Ability to read, understand, and contribute to open-source code

Valued Assets

• Experience with ML projects, even academic or personal
• Docker (images, Compose, volumes)
• Kubernetes (even basic notions)
• MLflow or an experiment tracking tool
• Notions of cloud or infrastructure
• Interest in time series or energy data

Practical Information

• Location: CAMPUS TRANSFO Jonage
• Start Date: September 2026
• Duration: 2 to 3 years
• Schedule: To be defined with your school