README.md
Welcome to my engineering notebook.
AI Quality Engineer • Data Analyst • Product Builder
Hi.
I'm MacDonald.
I build AI systems, analytics platforms, and data products that transform complex information into reliable decisions.
This website is my engineering notebook. It documents the systems I've built, the research I'm exploring, and the lessons I've learned while working across AI quality, data engineering, analytics, and product development.
PHILOSOPHY.md
How I think about building software.
Tools change. Frameworks change. The principles behind good engineering rarely do. These ideas guide how I approach AI, analytics, software engineering, and product development.
> Reliable AI begins with reliable data.
> Documentation is part of engineering, not an afterthought.
> Dashboards should answer questions, not create more.
> Simple systems are easier to trust than clever ones.
> Every project should leave behind reusable knowledge.
CURRENT_FOCUS.md
What I'm focused on right now.
My interests evolve over time, but they consistently revolve around building reliable systems, improving decision making with data, and exploring practical applications of artificial intelligence.
AI Quality Engineering
Designing evaluation pipelines, benchmarking large language models, and improving the quality and reliability of AI systems through structured human feedback.
Data Analytics
Building analytics solutions and executive dashboards that transform operational data into actionable business decisions.
Agricultural Intelligence
Developing AgriGraph AI and AgriMANET Scheduler to explore knowledge graphs, intelligent scheduling, and AI-powered agricultural systems.
Open Source
Building public projects that document ideas, research, and practical engineering work rather than isolated code samples.
REPOSITORIES.md
Repositories
Every project begins with a problem. These repositories document the thinking, engineering decisions, implementation, and outcomes behind the systems I've built.
AgriGraph AI
Artificial Intelligence • In Development • 2026
Role
Founder • AI Engineer
Why
Built to explore how knowledge graphs can unify fragmented agricultural datasets into an explainable intelligence layer for farmers, researchers, and policymakers.
Overview
AgriGraph AI transforms disconnected agricultural datasets into a knowledge graph that captures relationships between crops, weather, soil, diseases, markets, and farming activities. The goal is to enable explainable AI systems that can reason over agricultural information instead of relying solely on isolated datasets.
Stack
python • networkx • pandas • fastapi • knowledge-graphs • neo4j
Impact
> Designed a scalable agricultural knowledge graph architecture.
> Established a reusable ontology for agricultural entities.
> Explored explainable AI through graph reasoning.
> Created a foundation for future decision-support systems.
Repository
AgriMANET Scheduler
Research • Research • 2026
Role
Research Engineer
Why
Built to investigate adaptive scheduling algorithms for heterogeneous agricultural telemetry operating over dynamic MANET environments.
Overview
A research implementation of an Adaptive Weighted Round Robin scheduler designed to improve packet delivery, fairness, and network utilization in agricultural Mobile Ad Hoc Networks.
Stack
python • simulation • networking • manet • algorithms
Impact
> Simulated dynamic agricultural communication networks.
> Compared adaptive scheduling strategies.
> Documented networking performance metrics.
> Produced reproducible research experiments.
Repository
FeedbackLoop
AI Quality • Completed • 2026
Role
AI Quality Engineer
Why
Built to demonstrate how structured human feedback improves AI recruiting assistants through evaluation pipelines inspired by RLHF workflows.
Overview
FeedbackLoop models an AI evaluation workflow where prompts, responses, annotations, reviewer agreement, and quality metrics are combined into a repeatable assessment pipeline.
Stack
python • prompt-engineering • rlhf • evaluation • llms
Impact
> Created reproducible evaluation workflows.
> Improved annotation consistency.
> Demonstrated AI quality engineering practices.
> Documented scalable review pipelines.
Repository
BiasGuard
AI Quality • Completed • 2026
Role
AI Quality Engineer
Why
Built to investigate practical techniques for identifying, measuring, and mitigating bias in machine learning systems before deployment.
Overview
BiasGuard provides a structured workflow for analysing model outputs across protected attributes, identifying fairness issues, and documenting mitigation strategies as part of an AI quality assurance process.
Stack
python • pandas • machine-learning • fairness • ai-evaluation
Impact
> Implemented fairness evaluation workflows.
> Documented reproducible bias testing.
> Promoted responsible AI engineering practices.
> Produced reusable quality assurance guidelines.
Repository
Healthcare Insights Dashboard
Data Analytics • Completed • 2025
Role
Data Analyst
Why
Built to demonstrate how healthcare data can be transformed into meaningful operational insights through interactive analytics.
Overview
An interactive Tableau dashboard exploring patient demographics, treatment outcomes, operational performance, and healthcare trends for executive decision-making.
Stack
tableau • sql • excel • data-visualization
Impact
> Designed executive dashboards.
> Improved KPI visibility.
> Simplified healthcare reporting.
> Presented complex datasets visually.
Repository
Football Dashboard
Data Analytics • In Progress • 2026
Role
Data Analyst
Why
Built to explore football analytics through interactive dashboards that support player evaluation, tactical analysis, and performance tracking.
Overview
A football analytics platform combining player statistics, team performance metrics, and visual storytelling into an interactive dashboard.
Stack
python • power-bi • sql • football-analytics
Impact
> Developed reusable sports analytics workflows.
> Applied interactive dashboard design principles.
> Explored performance metric visualisation.
Repository
CAREER_LOG.md
git log --author="MacDonald Uwachukwunenye"
commit a93f72d (HEAD -> ai-quality-engineering)
Author: MacDonald Uwachukwunenye
Date: Jan 2026
AI Data Quality Analyst / LLM Evaluation Specialist Evaluated AI-generated responses for reasoning quality. Designed evaluation workflows. Improved model reliability.
Stack
python • sql • llm-evaluation • prompt-engineering
commit f81bc0a
Author: MacDonald Uwachukwunenye
Date: Nov 2025
Senior Data Analyst / AI QA Analyst Standardized KPIs used across operational teams. Built Power BI dashboards for executive reporting. Applied SQL and Python to improve reporting accuracy.
Stack
power-bi • sql • python • analytics • business-intelligence
commit 92ea1cd
Author: MacDonald Uwachukwunenye
Date: Jun 2023
Analytics Consultant Reduced operational costs by approximately 20%. Introduced reporting standards. Built KPI dashboards.
Stack
power-bi • excel • sql
commit d41a8e1
Author: MacDonald Uwachukwunenye
Date: Feb 2022
Early analytics work through Mshel Homes, ALX Africa, Fred Brandon FLAMES Foundation, and Edwin Kiagbodo Clark Foundation. Developed research methods, exploratory analysis, dashboard design, and stakeholder reporting.
Stack
research • statistics • python • sql • power-bi • excel
git status
On branch ai-quality-engineering Changes not staged for commit: Building explainable AI systems Researching knowledge graphs Shipping open-source software Learning Kubernetes nothing to commit, working tree clean for now
RESEARCH.md
Research
I enjoy exploring ideas before they become products. Most of my work starts as research, evolves into experiments, and eventually becomes an open-source project or production system.
Knowledge Graphs for Agricultural Intelligence
Active
Exploring graph-based data models to connect agricultural datasets and improve explainability for AI-driven decision support.
LLM Evaluation Frameworks
Active
Studying scalable evaluation methods, human feedback workflows, and quality metrics for large language models.
Applied AI Quality Engineering
Active
Researching practical approaches for benchmarking, annotation quality, prompt evaluation, and production AI systems.
LAB_NOTES.md
Lab Notes
Short ideas, experiments, and notes from my work.
2026-06-28
Building an Engineering Notebook
Redesigned my portfolio around an engineering notebook instead of a traditional personal website.
2026-06-24
AI Evaluation Pipelines
Explored practical methods for evaluating LLM responses using structured rubrics and human feedback.
2026-06-18
Knowledge Graph Research
Investigated graph-based data models for agricultural intelligence and explainable AI systems.
2026-06-10
Power BI Design Patterns
Documented reusable dashboard layouts and KPI reporting techniques for executive stakeholders.
OPEN_SOURCE.md
Open Source
I believe public work is the best evidence of engineering ability. Every repository is treated as documentation of a problem, the thinking behind it, and the solution that emerged.
CONTACT.md
Get in touch
I'm always interested in conversations about AI quality engineering, data platforms, analytics, open source, and ambitious engineering projects.