ML Engineer - Power

Kpler; Paris; Sur place; EUR 60 000 - 90 000; Plein temps

Résumé du poste

Kpler is seeking a Machine Learning Engineer in Paris to design, deploy, and scale predictive models powering our commodity and energy intelligence platforms. You will collaborate with data scientists, data engineers, and product teams to turn research into production-grade software that delivers real-time insights.

You will build and maintain ML pipelines, manage time-series data, and establish robust MLOps practices while ensuring code quality and agile delivery.

Qualifications

• Proficient in Python with production-grade code experience.
• Experience with large-scale ML workflows and data pipelines.
• Strong knowledge of databases and time-series data management.

Responsabilités

• Architect and deploy production ML pipelines and microservices.
• Bridge research prototypes to scalable Python applications.
• Design and optimize PostgreSQL schemas for time-series data.
• Implement MLOps practices including training, backtesting, and versioning.
• Build clean data ingestion and transformation pipelines.
• Write modular, testable code and participate in CI/CD and Agile processes.

Connaissances

Python

PostgreSQL

Data engineering

Machine Learning

Git

Agile

English

Outils

Docker

Kubernetes

Airflow

MLflow

Kafka

Kubeflow

PyTorch/TensorFlow

Description du poste

At Kpler, we are dedicated to helping our clients navigate complex markets with ease. By simplifying global trade information and providing valuable insights, we empower organisations to make informed decisions in commodities, energy, and maritime sectors.

Since our founding in 2014, we have focused on delivering top-tier intelligence through user-friendly platforms. Our team of over 850 experts from 69 countries works tirelessly to transform intricate data into actionable strategies, ensuring our clients stay ahead in a dynamic market landscape. Join us to leverage cutting-edge innovation for impactful results and experience unparalleled support on your journey to success.

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

What you'll need (Must-haves)

• 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).

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?

We make things happen

We act decisively and with purpose, going the extra mile.

We buildtogether

We foster relationships and develop creative solutions to address market challenges.

We are here to help

We are accessible and supportive to colleagues and clients with a friendly approach.

Our People Pledge

Don’t meet every single requirement? Research shows that women and people of color are less likely than others to apply if they feel like they don’t match 100% of the job requirements. Don’t let the confidence gap stand in your way, we’d love to hear from you! We understand that experience comes in many different forms and are dedicated to adding new perspectives to the team.

Kpler is committed to providing a fair, inclusive and diverse work-environment. We believe that different perspectives lead to better ideas, and better ideas allow us to better understand the needs and interests of our diverse, global community. We welcome people of different backgrounds, experiences, abilities and perspectives and are an equal opportunity employer.

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