ML Engineer - Power
Paris
- Pay
- Pay not listed
- Work setting
Hybrid — work setting source
Work setup in the employer’s posting: hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type Full-time
Employment type in the employer’s posting: Full-time
From the employer’s posting
What you’ll work on
Full postingAs 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.
Design, build and maintain production ML workflows for power market forecasting
Turn ML prototypes into scalable, production-ready Python applications
Design PostgreSQL schemas for high-throughput time-series and event data
Set up automated training, backtesting and model and feature versioning
Build clean, validated ingestion and transformation pipelines
From the employer’s posting
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.
Design, build, and maintain production-grade machine learning workflows and microservices for power market forecasting and electricity grid modeling.
Transition statistical and machine learning prototypes from initial experimentation into scalable, production-ready Python applications.
Design and optimize database schemas in PostgreSQL to handle high-throughput time-series data, event streams, and normalization routines.
Establish automated model training, backtesting, evaluation, tuning, and feature/model versioning standards across deployments.
Construct clean ingestion and transformation pipelines, ensuring high integrity, validation, and low-latency access across analytical models.
Tools in this posting
- Python
- AWS
- Docker
- GCP
- Kafka
- Kubernetes
- MLflow
- PostgreSQL
- Airflow
Source — Tool mentions in context
- 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.
- 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. Experience & Background
- 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.
Nice-to-haves - 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.
- 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). We are a dynamic company dedicated to nurturing connections and innovating solutions to tackle market challenges head-on. If you thrive on customer satisfaction and turning ideas into reality, then you’ve found your ideal destination. Are you ready to embark on this exciting journey with us?
- 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).
- 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.
- 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.
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Sep 25, 2026 · 36 days ago
- Recorded sightings
- 30
- Last seen by us
- Nov 1, 2026
- Employer says posted
- Sep 23, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
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Job description
About 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 Responsibilities
-
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.
Experience & Background
-
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.
Nice-to-haves
-
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).
Employment type
Full-time
Source details
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- Pay
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- Location & working pattern
Paris
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- Work authorization
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