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Paris, Île-de-France, France
Hybrid
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.
Architect 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).
