Machine Learning Engineer (MLOps)

Location: Hybrid in Denmark, with locations in Copenhagen, Aarhus, Kolding, or Aalborg

Company

hirify.global is a technology company providing an operating data platform that connects people, assets, and processes in the construction industry.

What you will do

• Turn machine learning models and experiments into reliable, maintained production systems.
• Build and improve ML workflows from feature preparation and reproducible training through evaluation, deployment, monitoring, and retraining.
• Develop hands-on ML workflows in Databricks, including experiment tracking and model management with MLflow.
• Deploy and monitor models using cloud platforms and workflow orchestration tools.
• Collaborate with data scientists, data engineers, and product teams to connect engineering decisions with customer needs.
• Work with equipment, sensor, and contextual data to support predictions and insights for construction operations.

Requirements

• Experience with MLOps and taking machine learning solutions into production.
• Strong Python skills and software engineering practices, including testing, version control, code reviews, and CI/CD.
• Understanding of the complete ML lifecycle, including deployment, monitoring, and retraining.
• Ability to assess model performance, identify data leakage and drift, and turn experimental code into maintainable systems.
• Experience with cloud platforms and tools for orchestrating ML workflows and serving predictions.
• Ability to make trade-offs between reliability, performance, and cost and communicate technical choices clearly.

Nice to have

• Strong hands-on experience with Databricks and MLflow.
• Experience training, tuning, and validating machine learning or deep learning models.
• Experience with time series, sensor data, geospatial data, or distributed processing.

Culture & Benefits

• Hybrid work in an international environment with hubs across the globe.
• Collaboration with people from data science, data engineering, product, and engineering.
• Knowledge sharing, feedback, and support for personal and professional development.
• Opportunity to work on technology that improves safety, reduces downtime, and supports sustainable construction operations.

Hiring process

• Virtual meet and greet with a Talent Acquisition Partner.
• Deeper conversation about experience, working style, the team, and day-to-day challenges.
• Assignment-specific interview with a prepared case presentation, followed by references and background checks where required.