Zenner LeakAI
Machine learning leak scoring paired with agentic triage, Teams/SMTP alerting, and human work-order assignment—deployed as IIS-hosted APIs for AMI utility operations.
Daniel Agbaji
AI systems that move from research to production.
Machine learning engineer and doctoral researcher building deployable AI for utility operations, enterprise data platforms, and decision-critical workflows.
Focus
Where technical depth meets operational impact.
I help organizations ship AI that operators trust—models wired into APIs, data pipelines, dashboards, and human-in-the-loop service workflows.
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Applied machine learning
Anomaly detection, predictive maintenance, and scoring systems grounded in telemetry and enterprise data.
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Generative & agentic AI
RAG assistants, LLM workflows, and triage agents designed for real operational constraints—not demos.
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Enterprise data platforms
High-volume SQL systems, APIs, and cloud pipelines that keep AI connected to production truth.
Selected work
Systems built for scale and scrutiny.
A sample of production and research systems spanning utility AI, enterprise engineering, and scholarly tooling.
Enterprise AI Utility Operations Assistant
LLM-powered operational assistant using retrieval-augmented generation for intelligent troubleshooting, semantic search, and knowledge retrieval across utility workflows.
Water Anomaly & IoT Battery Prediction
Feature pipelines and Isolation Forest scoring for water anomalies, plus telemetry-driven battery-life risk models for AMI devices—integrated with dashboards and service APIs.
AMI Customer & Data Platforms
Enterprise applications supporting millions of utility records—customer portals, Windows services, multi-database routing, tenant upgrades, and reporting for billing and field operations.
Generative AI Research Assistant
Semantic search and document intelligence workflows for literature synthesis—bridging scholarly research practice with production LLM patterns.
Experience
A decade building systems people depend on.
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Software Engineer / Utility Data & AI Systems
Zenner USA
2022 — Present
Build IIS-hosted .NET/C# APIs, Angular portal features, Windows services, and SQL pipelines for AMI/AMR operations. Prototype ML and agentic AI for leak detection, IoT battery prediction, and human-in-the-loop service triage across high-volume meter systems.
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Teaching Fellow / Data Science Instructor
University of North Texas
2022 — Present
Teach and mentor 90+ master’s students in data analysis and knowledge discovery. Design Python and applied AI labs that translate research concepts into job-ready workflows.
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Full-Stack Software Engineer
FinLocker
2020 — 2021
Delivered ASP.NET Core and Angular security services protecting web and mobile inputs against XSS, REST APIs with EF Core, Azure pipelines and releases, and production debugging across Agile delivery with engineering, QA, and business partners.
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Software Developer Associate
Koch Industries / Flint Hills Resources
2020 — 2021
Built Java/Spring Boot microservices, C#/.NET and Xamarin components, and analytics surfaces with Power BI, Splunk, and Tableau—backed by AWS, SQL Server, Oracle, DynamoDB, and Redshift.
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Software Developer
AfriHUB Nigeria Limited
2014 — 2017
Developed REST APIs and enterprise applications with Java Spring and ASP.NET MVC, transactional data systems, CI/CD, and automated testing—while teaching programming in a distance learning institute.
Expertise
The stack behind the outcomes.
AI & machine learning
Generative AI, LLMs, LangChain, RAG, OpenAI APIs, scikit-learn, ML.NET, TensorFlow, PyTorch, vector databases, prompt engineering, anomaly detection, time-series scoring
Engineering
Python, C#, Java, TypeScript, JavaScript, FastAPI, ASP.NET Core, Spring Boot, REST APIs, microservices, Angular, IIS, Windows Services
Data & cloud
SQL Server, PostgreSQL, MySQL, Oracle, DynamoDB, Redshift, Azure, AWS, Docker, Kubernetes, Azure DevOps, GitHub Actions, Power BI, Tableau
Research & writing
Scholarship that informs practice.
Peer-reviewed work on AI adoption, technology literacy, and information systems—alongside a technical book for practitioners.
- 533+ citations Artificial intelligence in developing countries: The impact of generative AI technologies for development
- 285+ citations Perceptions toward artificial intelligence among academic library employees
- 92+ citations Information literacy, data literacy, privacy literacy, and ChatGPT
- Book Python for Information Professionals: How to Design Practical Applications to Capitalize on the Data Explosion
Dissertation focus: Navigating Challenges: Implementing Generative Artificial Intelligence in Small and Medium-Sized Utility Enterprises to Leverage Big Data · Full Google Scholar profile
About
Engineer. Researcher. Teacher.
Based in Dallas, Texas, I work at the intersection of production software engineering and applied AI research. My day job is shipping systems for utility and enterprise environments; my academic work examines how organizations adopt generative AI responsibly and effectively.
I am a Mark E. Rorvig Fellow at the University of North Texas, pursuing a Ph.D. in Data Science with a concentration in machine learning and artificial intelligence.
- Ph.D. Data Science (AI/ML) — University of North Texas
- M.S. Instructional Design & Technology — Emporia State University
- M.L.I.S. Library & Information Science — Emporia State University
- B.Tech. Information Technology — Federal University of Technology, Minna
- Certificate Full-Stack Java Software Development — Claim Academy
Contact
Let’s build something consequential.
Available for AI/ML consulting, architecture reviews, and full-time opportunities with teams shipping intelligent systems into production.