Experience

Machine Learning Engineer – Kiewit Corporation

April 2024 – Present

Designed, deployed, and monitored production machine learning models that identified over 65% of safety incidents across 400+ large-scale construction projects using historical and real-time operational data
Built end-to-end machine learning pipelines in Python using Pandas, Polars, Scikit-Learn, Statsmodels, Snowflake, and SQL, covering feature engineering, model training, deployment, monitoring, and performance evaluation
Developed scalable MLOps workflows using Docker, Dagster, and cloud infrastructure to automate data ingestion, model execution, reporting, and production deployments
Designed and deployed LLM-powered applications and AI agents to automate schedule analysis, enterprise knowledge retrieval, and operational decision-support workflows
Built interpretable AI solutions and executive dashboards using Explainable AI (XAI) techniques, enabling stakeholders to understand model predictions and operational risk
Led large-scale analytical studies on schedule performance, labor productivity, and indirect cost forecasting, delivering executive reports that informed enterprise planning and operational strategy

January 2017 – April 2024

Conducted research in Explainable Artificial Intelligence (XAI), developing methodologies to improve the transparency, interpretability, and trustworthiness of machine learning systems
Proposed and evaluated novel approaches for model interpretability, integrating SHAP, Partial Dependence Plots (PDP), Wasserstein Distance, and Formal Concept Analysis (FCA) to compare and explain machine learning models
Designed scalable machine learning pipelines and data engineering workflows, integrating heterogeneous datasets exceeding 21M+ records to support predictive maintenance research
Authored 4 peer-reviewed publications spanning XAI, human-in-the-loop AI, interactive visualization, and predictive analytics, presenting findings at academic and technical conferences