ML Engineer: Power Grid Intelligence

Carbon Data Solutions; Paris; Sur place; EUR 70 000 - 110 000; Plein temps

Résumé du poste

Kpler is seeking a Machine Learning Engineer to build and deploy predictive models powering global commodity, energy, and maritime intelligence platforms. You will collaborate with Data Scientists, Data Engineers, and Product teams to translate data flows into real-time insights for trading firms and analysts.

Responsibilities include deploying ML pipelines, bridging research to production, and ensuring robust MLOps and data ingestion practices across distributed systems.

Qualifications

• Two to five years of data-focused software engineering experience.
• Significant experience with large production Python codebases (not just notebooks).
• Deep understanding of electricity-grid fundamentals including generation and transmission.
• Experience with PostgreSQL, data design, normalization and time-series/event data handling.
• Experience in data science and ML research: statistics, hypothesis testing, training, evaluation, backtesting, tuning, model selection.
• Practical MLOps experience, including model and feature versioning.

Responsabilités

• Architect and deploy ML pipelines for power market forecasting and electricity grid modeling.
• Bridge research and engineering by turning prototypes into production-grade Python apps.
• Design and optimize PostgreSQL schemas for time-series data and event streams.
• Establish automated model training, backtesting, evaluation, tuning, and versioning across deployments.
• Build clean ingestion pipelines ensuring data integrity and low-latency access for analytics.
• Write modular, well-tested Python code and contribute to CI/CD and Agile processes.

Connaissances

Python mastery

Data engineering

English communication

Git & Agile

PostgreSQL & time-series

MLOps & versioning

Statistics & ML research

Outils

Docker

Kubernetes

Apache Airflow

Kubeflow

MLflow

Apache Kafka

Description du poste

Kpler is seeking a Machine Learning Engineer to build and deploy predictive models powering global commodity, energy, and maritime intelligence platforms. You will collaborate with Data Scientists, Data Engineers, and Product teams to translate data flows into real-time insights for trading firms and analysts.

Responsibilities include deploying ML pipelines, bridging research to production, and ensuring robust MLOps and data ingestion practices across distributed systems.