NDAH-E Analytics delivers AI engineering and data platform solutions for organizations that need reliable, scalable systems. We design and deploy machine learning and LLM-powered products with production-grade architecture that integrates directly into real business and clinical workflows. Our teams balance scientific rigor with engineering execution, with deep domain expertise across healthcare and life sciences.
About
Team
AI Engineering Lead
I build and deploy ML/NLP systems in production, from modeling and API design to deployment and runtime monitoring. I focus on modular AI architecture that integrates directly into healthcare, bioinformatics, and applied enterprise workflows.
- Production systems: Built and deployed ML/NLP systems in production environments
- Architecture capability: Designed modular AI APIs and cloud-native data workflows
- Lifecycle delivery: Experimentation, deployment, monitoring, and optimization
- Languages/frameworks: Python, FastAPI, Docker, orchestration frameworks, AWS/GCP
- Industries: Healthcare, bioinformatics, life sciences, enterprise AI
- Email: elvis.ndah@ndaheanalytics.com
Statistical Modeling and Experimentation
I translate statistical rigor into deployable AI decision systems, helping teams move from analysis to production execution. My work supports end-to-end delivery: evaluation strategy, deployment readiness, and performance tracking in real operations.
- Production delivery: Built model-driven decision workflows for operational teams
- Platform capability: Evaluation frameworks, model governance, and monitoring design
- Methods: Advanced statistics, experimentation, forecasting, causal analysis
- Industries: Healthcare, life sciences, and data-intensive enterprise environments
- Email: nji.abatih@ndaheanalytics.com