MLOps Engineer: Real-Time Energy Forecasting & Control

Companion.energy; Oost-Vlaanderen; Hybride; EUR 60 000 - 90 000; Plein temps

Résumé du poste

Companion.energy is seeking an experienced MLOps Engineer to build the production-ready infrastructure for forecasting and control models across hundreds of assets. You’ll own deployment, backtesting at scale, and real-time monitoring of algorithms that drive millisecond decisions in energy markets.

The role emphasizes end-to-end MLOps—from packaging and versioning to monitoring and alerting—within a fast-growing startup focused on energy optimization and risk management.

Qualifications

• Strong Python software engineering and maintainable code practices.
• Experience taking ML models to production with deployment, monitoring, and rollback.
• Fluent in MLOps toolchain: experiment tracking and model registry (MLflow or equivalent), orchestration (Dagster, Airflow, Prefect).
• Familiar with containers, Kubernetes, CI/CD, and infrastructure as code.
• Experience with drift detection, data quality, and model-performance monitoring (Evidently, Great Expectations, Soda).
• Time series as a core problem: late/out-of-order data, backfills, and careful train/test separation.
• SQL skills and robust data pipelines; streaming or near-real-time systems (Kafka).
• Knowledge of energy systems: electricity markets, load forecasting, asset dispatch, balancing mechanisms.
• Previous startup/scale-up experience.

Responsabilités

• Package, deploy, version, and rollback forecasting and control models; ensure reproducible training and a clear promotion path to production.
• Monitor models in real time feeding market decisions with stringent latency requirements.
• Operate cross-asset deployment across hundreds of assets with data quality considerations.
• Ensure time-series correctness, handling irregular intervals, gaps, and backfills for backtests.
• Build scalable backtesting infrastructure replaying historical data across portfolios.
• Optimize real-time performance as intraday trading expands.

Connaissances

Python

MLOps

MLflow

Dagster

Airflow

Kubernetes

CI/CD

Infra as Code

Prometheus

Grafana

Evidently

Great Expectations

Soda

Time Series

SQL

Kafka

Feast

TimescaleDB

PostgreSQL

Energy markets

Startup experience

Outils

MLflow

Dagster

Airflow

Kubernetes

Docker

Prometheus

Grafana

Feast

TimescaleDB

PostgreSQL

Kafka

Description du poste

Companion.energy is seeking an experienced MLOps Engineer to build the production-ready infrastructure for forecasting and control models across hundreds of assets. You’ll own deployment, backtesting at scale, and real-time monitoring of algorithms that drive millisecond decisions in energy markets.

The role emphasizes end-to-end MLOps—from packaging and versioning to monitoring and alerting—within a fast-growing startup focused on energy optimization and risk management.