AI/ML Engineer & Automation Specialist
I build production-grade AI/ML systems — RAG pipelines, multimodal retrieval, and LLM-powered agents — alongside n8n workflow automation that turns models into reliable, real-world software.
About Me
AI/ML Engineer & Automation Specialist
Hi, I'm Uche Maduabuchi Daniel, an AI/ML Engineer & Automation Specialist with a Computer Science degree from the University of Ibadan. I build production AI systems — RAG pipelines, multimodal retrieval, and LLM-powered agents — alongside n8n workflow automation that turns models into reliable, real-world software.
I'm passionate about shipping AI from notebook to production — wiring up evaluation, monitoring, and the messy automation glue that turns a model into a working product. Beyond building, I enjoy video games, exploring new ideas, and experimenting with emerging technologies. Always eager to push boundaries and ship something new.
Featured Blogs
The Reasoning Paradox: When Smarter Models Become Less Reliable
OpenAI's newest model invents fake tools 48 percent of the time. The older, less clever model only does it 16 percent. The training that makes AI smarter is making it less reliable.
Automating Alignment: When Models Audit Their Own Safety
AI agents just beat human safety researchers four-to-one on a closed-form problem in a fifth of the time. The catch is in what kind of problem still needs the human.
Closing the Open-Source Gap
Five major free AI models shipped in April 2026 that are good enough to do real work. The gap between paid AI and free AI just narrowed from a chasm to a step.
Featured Certifications
B.Sc. Computer Science
Bachelor of Science degree in Computer Science from one of Nigeria's premier universities, providing a strong foundation in computing theory and practical software development.
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Deep Learning Specialization
Comprehensive specialization covering neural networks, deep learning, and practical applications in computer vision and natural language processing.
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Deep Learning A-Z 2025: Neural Networks, AI & ChatGPT Prize
Hands-on 22.5-hour course covering supervised and unsupervised deep learning techniques across vision, sequence, and generative tasks, updated with modern transformer and ChatGPT-era workflows.
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Tech Stack
