Highly accomplished AI/ML Engineer and Researcher with a multidisciplinary background in Civil Engineering, specializing in the end-to-end development and deployment of AI-driven solutions. Expert in Machine Learning, Computer Vision, and Data Science, leveraging Python, TensorFlow, PyTorch, and cloud technologies to solve complex challenges in agriculture, infrastructure, and intelligent systems. Proven ability to build scalable ML pipelines from data ingestion to API deployment, driving significant improvements in reliability, scalability, and operational performance.
AI Research & Deployment Engineer
Ndara, N/A, Nigeria
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Summary
Leads the research, development, and deployment of advanced AI solutions, focusing on predictive deep learning architectures and scalable MLOps practices.
Highlights
Spearheaded the research and implementation of advanced predictive deep learning architectures, optimizing model performance for complex AI applications.
Engineered reproducible experimentation pipelines, integrating structured model evaluation practices to ensure robust model validation and accelerate deployment cycles.
Executed comprehensive feature engineering and rigorous validation for structured and time-based datasets, improving model accuracy and reliability.
Authored technical architecture documentation, facilitating the scalable and efficient deployment of AI solutions across diverse platforms.
Collaborated effectively in Agile sprints, aligning structured documentation with UK/NHS workflows to ensure seamless project delivery and compliance.
Data Science/ ML Engineer Consultant
N/A, N/A, Canada
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Summary
Consulted on data science and ML engineering projects, specializing in end-to-end ML pipeline development and predictive risk modeling for healthcare applications.
Highlights
Architected and deployed an end-to-end ML pipeline, integrating CSV, PostgreSQL, and API endpoints, to power predictive risk modeling solutions.
Developed therapy dropout prediction models, achieving strong classification performance (AUC-optimized) to enhance patient retention strategies.
Implemented K-Means clustering with PCA for advanced behavioral segmentation, providing data-driven insights for strategic decision support.
Engineered production-ready REST APIs using Flask, enabling seamless and scalable model inference for real-time applications.
Containerized ML model deployments using Docker, ensuring reproducibility, scalability, and efficient environment management.
AI Research & Deployment Engineer
N/A, N/A, Nigeria
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Summary
Developed and deployed conversational AI backend systems, enhancing multi-turn dialogue capabilities and optimizing model inference efficiency.
Highlights
Developed robust conversational AI backend systems, enabling multi-turn dialogue capabilities for enhanced user interaction and engagement.
Constructed structured REST API endpoints, incorporating robust database migration management for reliable data integrity and system scalability.
Optimized model inference efficiency and reduced backend response latency, significantly improving overall system performance and user experience.
Implemented comprehensive CI/CD practices, facilitating seamless, version-controlled deployment updates and reducing release cycles.
Chief Engineer
Abuja, N/A, Nigeria
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Summary
Led infrastructure maintenance and transportation engineering projects, supervising operations and supporting data-driven planning for road networks.
Highlights
Led critical infrastructure maintenance and transportation engineering projects, ensuring operational continuity and adherence to safety standards.
Supervised complex engineering operations, conducting technical assessments and overseeing project implementation across multiple sites.
Provided strategic support for data-driven infrastructure planning and maintenance strategies, enhancing decision-making efficiency and resource allocation.
Contributed to diverse road engineering, transportation, and maintenance projects, applying expertise in civil infrastructure design and execution.
Facilitated robust project coordination and developed comprehensive technical documentation, streamlining communication and project workflows.
Data Scientist / ML Engineer
N/A, N/A, Nigeria
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Summary
Developed and deployed ML models for renewable energy forecasting, pricing optimization, and churn prediction, delivering actionable business intelligence.
Highlights
Developed advanced renewable energy forecasting models, accurately predicting wind turbine power output by leveraging diverse environmental features.
Conducted in-depth feature correlation analysis and optimized regression models, improving prediction accuracy for critical business applications.
Engineered ML-driven pricing optimization systems, directly supporting revenue decision-making and maximizing profitability for clients.
Performed comprehensive cohort retention analysis and developed churn prediction models for e-commerce clients, informing targeted retention strategies.
Delivered actionable business intelligence dashboards using Power BI and Tableau, providing critical insights for strategic business decisions.
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M.Eng.
Civil Engineering
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B. Eng
Civil Engineering
Issued By
N/A
Issued By
Alx
Issued By
World Quant University
Issued By
Council for the Regulation of Engineering in Nigeria
Issued By
Nigerian Society of Engineers
Deep Learning, Predictive Analytics, Machine Learning, Computer Vision, Data Modelling, MLflow, Scikit-learn, TensorFlow, PyTorch, K-Means, PCA, Regression Models, Classification Models, Natural Language Processing (NLP), Conversational AI.
Data Visualization, Statistical Analysis, Data Engineering, SQL Systems, Feature Engineering, Cohort Analysis, Churn Modeling, Business Intelligence, Pandas, NumPy, Matplotlib, Power BI, Tableau, Looker.
Python, JavaScript, API Development, REST APIs, Flask, Git, GitHub, VSCode, CI/CD, Docker, Pipelines, Actions.
Azure, AWS, GCP, Infrastructure Analytics, Containerization.
Problem Solving, Technical Research, Teamwork and Collaboration, Agile Sprints, Structured Documentation.
Summary
This project applies computer vision techniques to identify diseases in cassava plants. It aims to enhance agricultural efficiency, which is vital for economies where farming is the primary backbone.
Summary
A robust interactive application built using the Omeife API. It features seamless Natural Language Processing (NLP) capabilities, providing both voice-based interaction and real-time language translation.
Summary
This project uses predictive modeling to forecast product demand. The goal is to optimize harvest schedules and distribution strategies to ensure resources are allocated efficiently.
Summary
A machine learning solution designed to analyze pricing data. It utilizes ML algorithms to refine and optimize pricing strategies for the jewellery industry, maximizing business value.
Summary
Built regression models predicting turbine power generation using wind speed, direction, pressure, and temperature. Performed structured EDA and feature importance analysis. Developed inference-ready modeling pipeline for forecasting applications.
Summary
Designed full-stack ML system for therapy dropout prediction. Integrated PostgreSQL database pipelines and REST API endpoints. Implemented clustering-based persona segmentation.
Summary
Developed multi-turn AI API backend with structured database migration workflow. Designed scalable inference architecture using Flask.