More than four years of experience designing and deploying production-grade machine learning systems
AI systems, evaluation & governance
E.U. Eniang
AI/ML & Data Governance Lead
Data scientist and machine learning engineer working across model evaluation, MLOps, data systems, interpretability, and responsible AI governance.
E.U. Eniang is a data scientist and machine learning engineer with more than four years of experience building production-grade ML systems, data pipelines, and analytics infrastructure. His work spans machine learning, MLOps, model monitoring, data engineering, NLP, and cloud deployment.
Bio
E.U. Eniang is a data scientist and machine learning engineer with more than four years of experience designing, deploying, and monitoring production-grade machine learning systems across fintech, government, AdTech, and analytics environments.
His work spans the full machine learning lifecycle, including data preparation, feature engineering, statistical analysis, model development, deployment, monitoring, and continuous improvement. He has built time-series forecasting systems, recommendation systems, anomaly-detection pipelines, large-scale ETL workflows, real-time analytics infrastructure, and containerized machine learning applications using tools including Python, SQL, TensorFlow, PyTorch, Scikit-learn, Apache Spark, Docker, Kubernetes, AWS, and GCP.
Across previous roles, Eniang has worked with high-volume operational and financial datasets, including automated pipelines processing tens of millions of records monthly and large-scale data workflows supporting downstream machine learning and business intelligence. He has also implemented data-quality and validation frameworks, model-monitoring practices, and governance processes intended to improve the reliability and accountability of production systems.
His project work includes a machine-learning-based cybersecurity intrusion-detection system, an LLM-powered document-analysis engine built with Hugging Face Transformers and PyTorch, and a real-time price-tracking and threat-forecasting system with automated retraining and evaluation.
Eniang also brings experience translating technical outputs for non-technical stakeholders, including executive reporting, dashboards, model interpretation, and communicating analytical findings in ways that support decision-making.
At SILT, his work focuses on the technical and governance layer of AI-supported learning systems. He contributes to model evaluation, observability, experimental measurement, data pipelines, learner-data governance, and the design of technical systems that can be independently inspected, tested, and improved over time.
At SILT
At SILT, Eniang leads the technical evaluation and data-governance layer of the institute’s research. He supports model evaluation, observability, experimental instrumentation, data-quality systems, statistical measurement, and the responsible handling of learner-related data. His role is especially important in helping SILT distinguish between what an AI system appears to do and what can actually be measured, reproduced, monitored, and governed.
Selected Experience
Experience that informs the work.
Built forecasting systems achieving median MAPE below 8%
Designed automated ETL and data-processing pipelines handling tens of millions of records
Deployed containerized ML applications using Docker and Kubernetes
Implemented model-monitoring, data-quality, and governance workflows
Worked across fintech, government, AdTech, logistics, and non-profit analytics environments
Built NLP and LLM-based document-analysis systems using Hugging Face Transformers and PyTorch
Applied SHAP, LIME, and permutation importance for model interpretability
Experience communicating model outputs and analytical findings to non-technical stakeholders
Research updates
E.U. Eniang’s published work in progress.
Published weekly updates will appear here once the current case study begins.
