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Kpler is a global intelligence platform founded in 2014 that simplifies complex trade information for commodities, energy, and maritime sectors. With over 850 experts across 69 countries, the company transforms intricate data into actionable strategies for world-leading trading firms, industrial leaders, and analysts.
As an ML Engineer on the Power team, you will develop and deploy predictive models that power Kpler's commodity, energy, and maritime intelligence platforms. You will work closely with Data Scientists, Data Engineers, and Product teams to bridge machine learning experimentation and production-grade software delivery, directly transforming complex data flows into real-time, actionable insights.
Key Responsibilities:
- Architect and deploy production-grade ML pipelines and microservices for power market forecasting and electricity grid modeling
- Transition statistical and ML prototypes from 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
- Construct clean ingestion and transformation pipelines ensuring high integrity, validation, and low-latency access
- Write modular, well-tested Python code, participate in peer code reviews, CI/CD automation, and Agile delivery
Requirements (Must-haves):
- 2–5 years of experience as a data-focused software engineer
- Significant experience with large production Python codebases (not notebook-based work)
- Deep understanding of electricity-grid fundamentals: generation, transmission, and electricity markets
- Experience in data engineering with PostgreSQL or similar databases, including database design, data normalization, and time-series/event data management
- Proven experience in data science and ML research: statistics, hypothesis testing, model training, evaluation, backtesting, tuning, and model selection
- Practical experience in ML engineering, specifically model and feature versioning
- Confidence with Git, code reviews, Agile methodologies, and strong written/spoken English
Nice-to-haves:
- Experience deploying ML workloads on AWS or GCP using Docker and Kubernetes
- Familiarity with orchestration tools (Apache Airflow, Kubeflow, MLflow)
- Exposure to real-time streaming architectures (e.g., Apache Kafka)
