ML Engineer - Power

Kpler·Parismid

En corto

→Ingeniero de ML que construye modelos de pronóstico para mercados de energía y redes eléctricas.
→Desarrolla pipelines de producción en Python, gestionando datos en tiempo real y bases de datos PostgreSQL.
→Destacado: Trabajo directo con datos del sector eléctrico real, influenciando decisiones clave en mercados globales.

Strong written and spoken English required

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¿Qué piden?

✓2-5 años como ingeniero de datos o software con enfoque en datos
✓Dominio avanzado de Python en entornos de producción
✓Conocimiento profundo de redes eléctricas: generación, transmisión, mercados
✓Experiencia en diseño y gestión de bases de datos PostgreSQL con datos en tiempo real
✓Habilidades en MLOps: versionado de modelos y características, backtesting, evaluación
✓Comunicación efectiva en inglés (escrito y hablado) y participación en metodologías ágiles

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

PythonPostgreSQLGitCI/CDAgileDockerKubernetesApache AirflowKubeflowMLflow

¿A quién escribirle en Kpler?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

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. 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. , 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.

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