Summary
- Machine Learning Engineer with 2+ years building production AI systems and a Ph.D. in Artificial Intelligence
- Experienced across the full ML lifecycle, from data engineering to deployment and monitoring
- Built scalable predictive analytics and decision-support solutions for enterprise operations
- Skilled in MLOps, LLMs, RAG, explainable AI, and cloud-based ML pipelines
Machine Learning Engineer – Kiewit Corporation
April 2024 – Present
- Designed, deployed, and monitored production ML models that identified over 65% of safety incidents across 400+ large-scale construction projects
- Built end-to-end ML pipelines in Python using Pandas, Polars, Scikit-Learn, Statsmodels, Snowflake, and SQL — from feature engineering through deployment and monitoring
- Designed and deployed LLM-powered applications and AI agents for schedule analysis, enterprise knowledge retrieval, and operational decision-support
Research Assistant – University of Nebraska Omaha
January 2017 – April 2024
- Conducted research in Explainable AI (XAI), developing methodologies to improve transparency, interpretability, and trustworthiness of ML systems
- Proposed novel interpretability approaches integrating SHAP, Partial Dependence Plots, Wasserstein Distance, and Formal Concept Analysis (FCA)
- Designed scalable ML pipelines integrating heterogeneous datasets exceeding 21M+ records; authored 4 peer-reviewed publications
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Ramsey A, Kale A, Kassa Y, Ricks B, Gandhi R. Toward Interactive Visualizations for Explaining Machine Learning Models. Proceedings of the Information Systems for Crisis Response and Management Conference, Omaha, NE, USA. 2023.
↗Kale A, Kassa Y, Ricks B, Gandhi R. A Comparative Assessment of Bridge Deck Wearing Surfaces: Performance, Deterioration, and Maintenance. Applied Sciences. 2023; 13(19):10883.
↗Kale A, Ricks B, and Gandhi R. New measure to understand and compare bridge conditions based on inspections time-series data. Journal of Infrastructure Systems 27.4 (2021): 04021037.