Too much data and big AI but no clarity? I design, deploy, and scale machine learning and GenAI systems that turn messy, real-world data into decisions people can act on.
AI / ML Engineer · Lagos, Nigeria
Core Capabilities
From raw, messy data to a monitored production system, judgment is applied at every step.
Predictive modelling, classification, forecasting and decision-support engines trained on messy, real-world data.
Retrieval-augmented generation, tool-using agents and multimodal reasoning — including the harness and loop design that makes agents reliable, not just clever.
Infrastructure-as-code, containerised services and monitored pipelines that take a notebook from idea to production.
ETL pipelines, dimensional modelling and BI reporting that keep decision-makers working from a single source of truth.
Quasi-experimental design, hypothesis testing and econometrics applied to education, social science and financial data.
Combinatorial routing, assignment and sequential-decision problems solved with constraint programming, graph structures and reinforcement learning.
How the Models Actually Work
Ten live, running visualisations of the techniques behind the projects above.
Featured Systems
Shipped systems and research builds — each with the stack, the reasoning, and where to see it work.
Every project below is personal work, built and published in my own time. Source code for professional work — including everything built for Wema Bank Plc — is covered by NDA and isn't shown here, though I'm glad to speak to the impact and approach in conversation.
A real-time disaster-intelligence system built for Africa. AIDER fuses live weather data, breaking disaster news and satellite imagery, then uses multimodal AI to generate actionable emergency briefs for any location on the continent.
An end-to-end retrieval-augmented generation pipeline on AWS, built as an AI Engineer Nanodegree capstone: PDFs are ingested into S3, indexed into Aurora Serverless Postgres with pgvector/HNSW, and served through a Bedrock Knowledge Base Agent that reasons over the corpus with Claude — with category-classification prompt validation keeping a heavy-machinery support bot on-topic.
“weather”
A voice-first personal AI assistant running on a single real-time speech pipeline. Maya holds natural conversation while calling out to a live toolbelt — weather, alarms and reminders, health tracking (sleep, water, mood, medication), Spotify playback, Telegram messaging, and an emergency SOS routine with repeating alerts and motion detection.
A LightGBM-powered application built during the 3MTT Nigeria fellowship to predict Multi-dimensional Poverty Index (MPI) and quantify how tech-skills training moves the needle on poverty reduction — open-sourced as a knowledge showcase for fellow analysts.
A real-world variant of the Travelling Salesman Problem: ~3,957 geolocated points assigned across 41 agents, with routes optimised for minimum combined distance and balanced per-agent workload, respecting Earth-curvature distances.
A two-part capstone turning 1.73M raw NYC green-taxi trip records (2017–2020) into a dispatcher-ready analytics model: Python cleans and profiles the data, while a SQL Server build/analysis pipeline answers demand, revenue and zone-performance questions for operations decision-making.
A data-storytelling app tracing Nepal's climate and disaster risk from 1990 to the 2026 Bhote Koshi flood — from hazard evidence, through exposure and vulnerability, to regression-backed evidence supporting a $100M resilience-fund allocation. Built on NASA POWER, HydroSHEDS, national census, DesInventar/UNDRR and UNDP RAPIDA data.
A purpose-built scraping pipeline that collected fellow-level training data from a specialised public database, feeding the dataset later used to train ESAAM's MPI model.
A proxy-backed scraping pipeline over the Oxylabs API that collects and parses live Indeed job postings, then rolls them up into a cleaned dataset with market insights on roles, locations and demand.
A real-time disaster-intelligence system built for Africa. AIDER fuses live weather data, breaking disaster news and satellite imagery, then uses multimodal AI to generate actionable emergency briefs for any location on the continent.
An end-to-end retrieval-augmented generation pipeline on AWS, built as an AI Engineer Nanodegree capstone: PDFs are ingested into S3, indexed into Aurora Serverless Postgres with pgvector/HNSW, and served through a Bedrock Knowledge Base Agent that reasons over the corpus with Claude — with category-classification prompt validation keeping a heavy-machinery support bot on-topic.
“weather”
A voice-first personal AI assistant running on a single real-time speech pipeline. Maya holds natural conversation while calling out to a live toolbelt — weather, alarms and reminders, health tracking (sleep, water, mood, medication), Spotify playback, Telegram messaging, and an emergency SOS routine with repeating alerts and motion detection.
A LightGBM-powered application built during the 3MTT Nigeria fellowship to predict Multi-dimensional Poverty Index (MPI) and quantify how tech-skills training moves the needle on poverty reduction — open-sourced as a knowledge showcase for fellow analysts.
A real-world variant of the Travelling Salesman Problem: ~3,957 geolocated points assigned across 41 agents, with routes optimised for minimum combined distance and balanced per-agent workload, respecting Earth-curvature distances.
A two-part capstone turning 1.73M raw NYC green-taxi trip records (2017–2020) into a dispatcher-ready analytics model: Python cleans and profiles the data, while a SQL Server build/analysis pipeline answers demand, revenue and zone-performance questions for operations decision-making.
A data-storytelling app tracing Nepal's climate and disaster risk from 1990 to the 2026 Bhote Koshi flood — from hazard evidence, through exposure and vulnerability, to regression-backed evidence supporting a $100M resilience-fund allocation. Built on NASA POWER, HydroSHEDS, national census, DesInventar/UNDRR and UNDP RAPIDA data.
A purpose-built scraping pipeline that collected fellow-level training data from a specialised public database, feeding the dataset later used to train ESAAM's MPI model.
A proxy-backed scraping pipeline over the Oxylabs API that collects and parses live Indeed job postings, then rolls them up into a cleaned dataset with market insights on roles, locations and demand.
Research & Scholarship
Peer-reviewed research and conference work in learning science and social research — the same rigor applied to every model shipped.
Journal Article · 2025
Journal of Teaching and Learning, 19(1), 107–130
Journal Article · 2024
Sapientia Foundation Journal of Education, Sciences and Gender Studies, 6(3), 235–251
Conference Presentation · 2024
12th International Mardin Artuklu Scientific Researches Conference, Mardin, Türkiye
Data Analytics & Business Intelligence Tutor · Baskenky
Teaching analytics engineering, dbt, SQL, Python, Power BI, and Git/GitHub workflows to aspiring data professionals — translating production data practice into a structured, mentor-led curriculum.
How relational, network-based machine learning — beyond flat tabular models — can capture the way African commodity and trade systems actually move together, especially under climate and macroeconomic shocks. Early-stage, ongoing work.
Applying predictive modelling and econometrics to banking and fintech risk problems — bridging his Financial Engineering training with production ML practice in a live banking environment.
Peer-reviewed research on interactivity and the Protégé Effect (teaching-to-learn) as retention factors in biology education — the original research thread his academic career started from.
Applying geospatial analysis, forecasting and NLP to public-interest problems — election-integrity detection, flood prediction, and poverty-index modelling among them.
Public datasets published for open use are on the way — check back soon, or follow on Kaggle for the first release.
Contributions to open-source projects outside his own repos — coming soon.
Career Trajectory
From biology first-class honours to fintech AI — a path built on statistics, research rigor and production engineering.
Feb 2026 — Present
Wema Bank Plc — Data Analytics & AI Team · Marina, Lagos, Nigeria
Mar 2025 — Jan 2026
Wema Bank Plc — Data Analytics & AI Team · Marina, Lagos, Nigeria
May 2024 — Jan 2025
Directorate of ICT, Olabisi Onabanjo University · Ago Iwoye, Ogun, Nigeria
Feb 2024 — Dec 2024
3MTT Nigeria — The Founding Network · Osogbo, Osun, Nigeria
Jul 2024 — Aug 2024
HNG Internship 11 · Remote, Nigeria
Jul 2024 — Aug 2024
KPMG US — Forage Tax Simulation · Remote, US
Jul 2023 — Feb 2024
Science & Technology Education Dept., Obafemi Awolowo University · Ife, Osun, Nigeria
Kaggle badges: Bronze Medal — Notebook: AIDER · Completed 5-Day Gen AI Intensive (Kaggle × Google) · Python Coder · Kaggle Community Member · 2 Years on Kaggle
Data Analyst & Cohort Secretary
HAVEK Leadership Academy · Jul 2024 — Present
Research Lead & Interim Secretary
Instride Pathways Youth Foundation · May 2024 — Present
Active Member
Humanity First Foundation · Oct 2024 — Present
Data Analysis Tutor
The Ireti Foundation · Present
Beyond Work
Outside the notebooks and dashboards — the things that actually run in the background.
Endorsements
Query the Brain, Visually
This is the actual map Time — the AI trained on part of me, below — answers from: experience, projects, research, skills, hobbies, philosophy, wired together as a living graph instead of a list. It's a fraction of what's actually in my head, but it's real, and it's what Time draws on when it talks to you.
Query the Brain
Time is trained on a slice of my brain — my work, my thinking, my way of answering — and replies as me, in first person. It's not the full me, honestly probably not even 10% of me, but it tries hard with what it's got. Find it again anytime via the floating icon.
Hey — I'm Time, a replica of part of Malik's brain: his work, his projects, his way of thinking, rebuilt to talk to. I don't know everything he does — honestly, probably not even 10% of it — but I answer as him, first person, doing my best with what I've got. Ask me anything.
Let's Talk
Whether it's a production ML system, a GenAI pipeline, or a research collaboration — reach out directly.