I'm an Applied AI and Machine Learning Engineer focused on building intelligent systems that work in production — not just in notebooks. My interest lies at the intersection of predictive modelling, LLM-powered applications, and data-driven decision support.

I hold a B.Tech in Electrical and Electronics Engineering from LAUTECH and an MSc in Artificial Intelligence and Data Science from the University of Hull.

My engineering work spans predictive modelling pipelines, anomaly detection systems, and end-to-end ML workflows — built and deployed across industries including energy, agriculture, and business intelligence. At Hankali Intel I led ML solution delivery for decision support, improving model reliability by 35% through iterative optimisation. At Africa Agility Foundation I built and evaluated classification models end-to-end, covering feature engineering, cross-validation, and performance tuning. Earlier, at Eko Electricity Distribution, I built anomaly detection models that contributed directly to recovering revenue from a dataset of over a million customer records.

My current focus is applied generative AI: retrieval-augmented generation, agentic systems, and LLM pipelines built with LangChain, FastAPI, and vector databases. I build systems that integrate into real workflows — reliable, maintainable, and useful beyond the demo.