Machine Learning Engineer (Energy Management)

TL;DR

Machine Learning Engineer (MLOps/AWS): Building production-grade ML solutions for energy management with an accent on scalable data processing and MLOps practices. Focus on designing end-to-end ML pipelines, implementing CI/CD for models, and optimizing workloads on AWS.

Location: Hybrid (Rotterdam). Work 40% at the office, 40% from home, and 20% flexibly. Limited work from abroad (up to 3 weeks/year) with manager approval.

Salary: €84,000 – €117,000 per year

Company

hirify.global is an energy company committed to accelerating the energy transition to become climate neutral by 2035.

What you will do

• Design and maintain end-to-end ML pipelines for training, validation, and production inference in batch and real-time environments.
• Develop scalable MLOps capabilities, including CI/CD pipelines, model versioning, automated testing, and drift detection.
• Build and operate Databricks-based data processing and machine learning workflows at scale.
• Manage MLflow for experiment tracking, model registry, and reproducible deployments.
• Deploy and optimize ML workloads on AWS, balancing performance, scalability, and cost.
• Build monitoring, alerting, and observability for production models (latency, throughput, data drift).

Requirements

• 4+ years professional experience in ML engineering and 2+ years owning MLOps/production ML systems.
• Proficiency in Scala for data processing/ETL on Spark and familiarity with the JVM ecosystem.
• Hands-on experience with Databricks and MLflow for large-scale processing and tracking.
• Strong AWS experience deploying and operating ML workloads.
• Experience building CI/CD for ML and containerized deployments using Docker and Kubernetes.
• Strong software engineering fundamentals: testing, design patterns, and code reviews.

Culture & Benefits

• Gross annual salary between €84,000 and €117,000, including an 8% holiday allowance.
• FlexBudget that can be paid out, used for extra holiday days, or saved.
• Performance-based bonus or collective profit sharing depending on the role.
• Hybrid working model: 40% office, 40% home, 20% flexible.
• Full commitment to personal and professional growth and development.
• Collaborative environment focusing on ownership and continuous learning.