Machine Learning Engineer - Production ML/AI

The Opportunity: You'll work on several machine learning problems, including: Construction Duration Prediction Improve an existing production model that predicts how long construction work will take. Permit Prediction Develop a model to predict how long it may take to obtain government permits required for construction. Cost Prediction Build a predictive model using historical material and labor expenses to estimate the cost of future construction work. Automated Construction Design Explore ML approaches that could help automate aspects of construction design, including trench placement, poles, and cable layouts.

Your Responsibilities: Analyze existing production models and identify opportunities for improvement. Build automated model retraining pipelines. Perform feature engineering and analyze historical data. Investigate model errors and develop solutions for underperforming scenarios. Determine when additional features are appropriate versus when a specialized model may be needed. Develop predictive models using structured and historical data. Build data pipelines supporting model training and feature engineering. Track experiments and model performance. Deploy and integrate ML applications into production environments.

Preferred Background: Machine Learning Engineering experience. Experience withpredictive modeling .Strong Python experience. Experience withXGBoost, LightGBM, or similar ML algorithms .Feature engineering and model evaluation. Experience building ML/data pipelines. Production ML / MLOps experience. AWS experience. MLflow or similar experiment-tracking experience. Strong problem-solving skills and the ability to investigate why models succeed or fail. Environment AWS | Python | XGBoost | MLflow | DVC | FastAPI | HashiCorp NomadThis is a great opportunity for someone who wants to work onreal predictive AI problems with measurable business impact, rather than purely theoretical ML research. GCS is acting as an Employment Business in relation to this vacancy.