ML Engineer – Power

Kpler

. Develop and deploy predictive models powering commodity, energy, and maritime intelligence platforms .

Posted 9/23/2026full-timeParis • FranceJuniorMid-LevelWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and deploying predictive models for energy and maritime intelligence, with a strong focus on machine learning workflows and production-grade Python applications. Proficient in data engineering practices, including PostgreSQL schema design and managing time-series data, while collaborating effectively within Agile teams.

Highest-signal resume keywords

Python Application DevelopmentPostgreSQL Database DesignMachine Learning EngineeringData EngineeringAgile Methodologies

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Predictive ModelingMachine Learning WorkflowsData NormalizationModel TrainingBacktestingFeature VersioningStatistical AnalysisTime-Series Data ManagementEvent Stream ProcessingCode Review

Soft Skills

Strong Written CommunicationStrong Spoken Communication

Tools & Technologies

GitDockerKubernetesAWSGCPApache AirflowKubeflowMLflowApache Kafka

Industry Keywords

Electricity Grid FundamentalsEnergy MarketsMaritime IntelligenceData ScienceMachine Learning

Tech Stack

Tools & technologies

AirflowApacheAWSDockerGoogle Cloud PlatformKafkaKubernetesMicroservicesPostgresPython

About the role

Key responsibilities & impact

Develop and deploy predictive models powering commodity, energy, and maritime intelligence platforms
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 into scalable, production-ready Python applications
Design and optimize PostgreSQL schemas for high-throughput time-series data, event streams, and normalization routines
Establish automated model training, backtesting, evaluation, tuning, and feature/model versioning standards
Construct clean data ingestion and transformation pipelines with high integrity, validation, and low-latency access
Write modular, well-tested Python code
Participate in peer code reviews, CI/CD automation, and Agile delivery processes
Collaborate with Data Scientists, Data Engineers, and Product teams

Requirements

What you’ll need

Approximately two to five years of experience as a data-focused software engineer
Significant experience working with large production Python codebases, rather than working exclusively in notebooks
Deep understanding of electricity-grid fundamentals, including generation, transmission, and electricity markets
Experience in data engineering, including PostgreSQL or similar databases, database design, data normalisation, and managing time-series and event data
Proven experience in data science and machine learning research, including statistics, hypothesis testing, model training, evaluation, backtesting, tuning, and model selection
Practical experience in machine learning engineering, specifically including model and feature versioning
Confidence working with Git, code reviews, and Agile methodologies
Strong written and spoken English
Nice to have: Experience deploying ML workloads on AWS or GCP using Docker and Kubernetes
Nice to have: Familiarity with Apache Airflow, Kubeflow, or MLflow
Nice to have: Exposure to real-time streaming architectures such as Apache Kafka

Benefits

Comp & perks

Full-time employment
Fair, inclusive and diverse work environment
Equal opportunity employer
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