We are looking for a Machine Learning Engineer to help build and finalize ML components for a solution that optimizes repair operations in the construction/maintenance domain. The goal is to use historical data to help dispatchers and technicians make faster and more accurate decisions, reduce unnecessary trips, and lower overall time and costs.
Project format: 4–6 week engagement, with both part-time and full-time options possible.
Main responsibilities * Design and implement ML models within the Azure ecosystem (Azure ML and related services) * Work with unstructured text data from historical Work Orders and convert it into structured, ML-ready features * Select and apply appropriate ML approaches and algorithms (for example, xgboost, random forest, BERT, TF-IDF-based models) depending on the task * Build models that: * generate semantically relevant keywords for typical problem patterns * recommend required parts for a given Work Order * forecast expected time and/or cost for a given Work Order * Validate and improve model performance, iterate on feature engineering * Collaborate with the team to integrate ML components into the overall solution (APIs, scoring services, etc.)
Core requirements * Strong experience working in the Azure ecosystem * Experience transforming unstructured data (primarily text) into structured features * Solid understanding of different ML approaches and when to use them * Proficiency in Python and common ML / NLP libraries * Experience working with noisy real-world business data * Good communication skills and ability to work in a dynamic environment
Nice to have * Experience with NLP (text classification, keyword extraction, semantic similarity) * Experience in operations optimization, field service management, or dispatching * Experience building time and cost estimation models
We offer * Long-term employment with competitive compensation, based on experience. * Possibility to work remotely. * An open, transparent, and fun work culture. * Multi-national team and collaborative work environment. * Continuous knowledge sharing with engaged co-workers. * Career and professional growth opportunities.
