Machine Learning Engineer

_Most machine learning never makes it to production. Your job is to make sure ours does. We are looking for an ML Engineer who turns models into reliable, maintained systems that construction companies actually use to make better decisions about their equipment and operations. You will work at the intersection of data science and software engineering, shaping how machine learning gets built, deployed, and improved at Trackunit. _**What’s in it for you? ** Bring machine learning into everyday use. Turn models and experiments into reliable products that help customers make better decisions about their equipment and operations. Shape how we build and run ML. Influence the tools, engineering practices, and deployment patterns that take solutions from an initial idea through to production and continuous improvement. Work on interesting data challenges. Explore how equipment, sensor, and contextual data can support useful predictions and insights for the construction industry. Build alongside people with different strengths. Collaborate with data scientists, data engineers, and product teams, combining scientific thinking with practical software engineering and customer understanding. Develop your craft and your career. Join an international environment with knowledge sharing, feedback, and support for your personal and professional development. **Who are you ideally? ** You have MLOps experience taking machine learning solutions into production and maintaining them beyond their initial release. You bring strong Python skills and solid software engineering practices, including testing, version control, code reviews, and CI/CD. Databricks expertise is a significant advantage. You ideally bring strong hands-on experience building ML workflows in Databricks, tracking experiments and managing models with MLflow, and deploying and monitoring models in production. You understand the ML lifecycle, from feature preparation and reproducible training to evaluation, deployment, monitoring, and retraining. You can work with data scientists to assess model performance, recognise issues such as data leakage and drift, and translate experimental code into maintainable systems. You have experience with cloud platforms and tools for orchestrating ML workflows and serving predictions. You can make sensible trade-offs between reliability, performance, and cost. You enjoy shaping solutions with others, explaining technical choices clearly, and connecting engineering decisions to customer needs. Experience training, tuning, and validating machine learning or deep learning models would be a plus. Experience with time series, sensor or geospatial data, or distributed processing would be a plus. You don’t need experience with every tool or data type listed. If you bring strong engineering skills and enjoy making machine learning useful in practice, we’d like to hear from you. ****Our hiring process ****Don't waste your time on writing the best possible cover letter for the jo