ML Engineer - Power

Kpler

Contract type

Ongoing

Work mode

On-site · On-site (see listing for address)

Experience

Mid-level · 3+ years

Job description

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 experimentation into scalable, production-ready Python applications
Manage time-series and event data systems: design and optimize PostgreSQL database schemas for 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 and participate in peer code reviews, CI/CD automation, and Agile delivery processes
Company mission
We are a dynamic company dedicated to nurturing connections and innovating solutions to tackle market challenges head-on

Core technologies

Python (Programming Language)AWSGoogle Cloud Platform (GCP)DockerKubernetes

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 working with PostgreSQL or similar databases, database design, data normalisation, and managing time-series and event data
Proven experience in data science and machine learning research, encompassing 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, supported by 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 orchestration tools such as Apache Airflow, Kubeflow, or MLflow
Nice-to-have: exposure to real-time streaming architectures (e.g., Apache Kafka)
Python, PostgreSQL, Git, AWS, GCP, Docker, Kubernetes, Apache Airflow, Kubeflow, MLflow, Apache Kafka
Python (Programming Language)
AWS
Google Cloud Platform (GCP)
Docker
Kubernetes