ML Engineer - Power

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 build
together

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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