ML Engineer - Power
Kpler has an opening for a ML Engineer - Power in Paris, France. It is listed as a full-time position.
DGJ summary of this job
Our own short read of this listing, built only from the facts it gives. The employer’s original text follows further down.
Kpler has an opening for a ML Engineer - Power in Paris, France. It is listed as a full-time position.
Reading the advert, the employer asks for English language skills.
Skills and tools named in the text include Python, PostgreSQL, Kafka, Airflow, AWS, GCP, Kubernetes and Docker.
No salary is given in this listing, so check the official pay figures in the DGJ Insight section below where they exist.
The advert does not mention visa sponsorship. If you would move to France for this job, check the official work permit route first (DGJ Insight lists it where we have it).
Dev Global Jobs found this job on Arbeitnow on Nov 15, 2026.
- Employer
- Kpler
- Location
- Paris, France
- Job type
- Full-time
- Pay
- Not stated by the employer
- Languages asked
- English
- Found
- Nov 15, 2026 on Arbeitnow
- Employer text
- 707 words, about 3 min to read
- Apply only on the official page (arbeitnow.fr). Dev Global Jobs is not the employer and never asks for a fee.
- Mirror the skills the advert names (Python, PostgreSQL, Kafka and Airflow) in your CV where you really have them; applicant tracking systems look for these words.
- Say your level in English plainly (for example B2 or C1).
- Read the employer’s full text below (about 3 min) before you apply.
- Build or check your CV with the free DGJ CV Builder.
DGJ Insight
Our own data about this role and place, from official statistics and our live job index.
Source: OECD Average annual wages, 2025
UN living-cost index for Paris (ICSC post adjustment 49.5), as of 1 Nov 2026.
Source: ICSC post adjustment classifications (icsc.un.org)
- Talent Passport: Qualified employees, researchers, artists, investors; multi-year permit
- Employee (salarié) visa: Employer work authorisation from the labour office
- Temporary worker visa: Contracts under 12 months
About the role
Full text from the employer, as published on Arbeitnow, arranged into sections. Nothing added or removed.
01About the Role
As a Machine Learning Engineer at Kpler, you will play a key role in developing and deploying predictive models that power our global commodity, energy, and maritime intelligence platforms. Working closely with Data Scientists, Data Engineers, and Product teams, you will bridge the gap between machine learning experimentation and production-grade software delivery. Your work will directly transform complex data flows into real-time, actionable insights that help world-leading trading firms, industrial leaders, and analysts make critical decisions.
Key ResponsibilitiesArchitect and deploy ML pipelines: Design, build, and maintain production-grade machine learning workflows and microservices for power market forecasting and electricity grid modeling.
Bridge research and engineering: Transition statistical and machine learning prototypes from initial experimentation into scalable, production-ready Python applications.
Manage time-series and event data systems: Design and optimize database schemas in PostgreSQL to handle high-throughput time-series data, event streams, and normalization routines.
Implement robust MLOps practices: Establish automated model training, backtesting, evaluation, tuning, and feature/model versioning standards across deployments.
Drive data engineering quality: Construct clean ingestion and transformation pipelines, ensuring high integrity, validation, and low-latency access across analytical models.
Champion software excellence: Write modular, well-tested Python code, actively participating in peer code reviews, CI/CD automation, and Agile delivery processes.
Software engineering foundation: Approximately two to five years of experience as a data-focused software engineer.
Python mastery: Significant experience working with large production Python codebases, rather than working exclusively in notebooks.
Domain knowledge: Deep understanding of electricity-grid fundamentals, including generation, transmission, and electricity markets.
Data engineering & databases: Experience in data engineering, including working with PostgreSQL or similar databases, database design, data normalisation, and managing time-series and event data.
DS & ML research rigor: Proven experience in data science and machine learning research, encompassing statistics, hypothesis testing, model training, evaluation, backtesting, tuning, and model selection.
MLOps & versioning: Practical experience in machine learning engineering, specifically including model and feature versioning.
Engineering practices & communication: Confidence working with Git, code reviews, and Agile methodologies, supported by strong written and spoken English.
Cloud platforms: Experience deploying ML workloads on AWS or GCP using Docker and Kubernetes.
Workflow orchestration: Familiarity with orchestration tools such as Apache Airflow, Kubeflow, or MLflow.
Streaming technologies: Exposure to real-time streaming architectures (e.g., Apache Kafka).
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