Abiodun Allison

Lagos, Nigeria · allisonabiodun@gmail.com

Experience

Junior Back-end Engineer

Tog Lab

  • Utilized a RAG workflow with LangChain, TypeScript, React, and Next.js to construct a chatbot. The company website content served as the knowledge base.
  • Employed Redis for session management and chat history.
  • Designed a multi-agent chat-bot using Flowise AI and Pinecone vector store.
  • Developed Python code to manage documents in the vector store using Unstructured and LangChain.
  • Deployed the solution via AWS Lambda, triggering on document changes in an S3 bucket.
  • Implemented CI/CD with AWS CodePipeline and managed infrastructure using AWS CDK.
  • Designed and maintained scalable backend services using NestJS

June 2023 - Present
Remote

Back-end Engineer Intern

Tog Lab

  • Developed REST APIs using NestJS for a platform that simplifies building and managing professional websites.
  • Designed and implemented PostgreSQL database schemas for efficient data retrieval and storage.
  • Implemented Redis caching to enhance performance and reduce database load.
  • Documented API endpoints using Swagger.
  • Collaborated with front-end developers to integrate APIs into web applications.

August 2022 - May 2023
Remote

AWS Cloud Instructor

HiiT Plc.

  • Instructed students on AWS core services—including compute, storage, networking, security, and databases—to prepare them for the AWS Cloud Practitioner exam.
  • Hands-on sessions were led using both the AWS console and the command line interface to reinforce practical skills.

June 2022 - September 2022
Remote

Machine Learning Engineer Intern

Rural Farmers Hub

  • Annotated farmland boundaries using geospatial imagery on Google Earth during the data collection phase for an automatic farm boundary detection project.
  • Executed a satellite imagery preprocessing workflow as part of a computer vision team using Python. The processed images were used to feed a convolutional neural network model to digitally map key soil nutrients.

August 2020 - October 2020
Remote

Projects

Toyota Classifier
Deployed web app for Toyota model classifier project. (Credit: Abiodun Allison)

Melanoma Detection Using EfficientNet+BiLSTM

Python, Tensorflow, Tensorflow Lite, Image clasification, Streamlit

Trained and deployed an hybrid pretrained EfficientNetB6 and Bidirectional LSTM model to predict whether the lesion in given image is benign or malignant. Dataset used was obtained from The International Skin Imaging Collaboration (ISIC) 2020 Challenge dataset.

August 2021

Fuel Efficiency Prediction

Python, Data cleaning, EDA, XGBoost

Implemented gradient boosting regression model and neural network to predict vehicles' miles per gallon (MPG) values, which is the primary measurement of a car's fuel efficiency. Auto MPG dataset was used which consists of a description of many automobiles in the late 1970s and early 1980s.

August 2021

Stroke Prediction

Python, Gradient boosting, Data storytelling, Streamlit, Heroku

Explored stroke prediction dataset and implemented a regularizing gradient boosting model to predict the likelihood of a patient having a stroke or not considering input parameters like gender, age, various diseases, and smoking status. The model was deployed using Streamlit built web app deployed on Heroku.

July 2021

Toyota Model Classifier

Python, Image preprocessing, Fast.ai, Streamlit

Built a simple web app that classifies different Toyota brands using Streamlit. Images for different Toyota models were gotten using Bing search API. After image cleaning and pre-processing, a pre-trained Resnet101 model was implemented classify the images using FastAI.

Febuary 2021

Analysis and Prediction of Health Insurance Subscription in Nigeria

Python, Data cleaning, EDA, Streamlit, Heroku

Created and deployed a web app that predicts whether an individual would take up a health insurance policy or not leveraging a machine learning classification model. Factors that most likely influence taking up a health insurance policy by an individual were also investigated. Dataset used was the Individual Recode section of the 2018 Nigerian Demographic and Health Survey DHS dataset.

November 2020

Volunteering

AI+ Volunteer Tutor

Data Science Nigeria AI Club for School

Through hands-on learning and class challenges/assignments, I'm training twelve primary school students (Grades 4-6) in programming using Scratch. Completed a catch-an-apple Scratch project with the students, and we are working on more projects.

September 2021 - July 2022
Lagos, Nigeria

Volunteer Tutor

Programming Workshop For Scientists In Africa

I volunteered as a group tutor for PWSAfrica, a 2-week workshop aiming to empower scientists in Africa with computer programming skills. Handled a break out group during coding sessions where I helped participants practise what was learnt, from introduction to Python to Machine Learning. My group worked on a Book Recommender project at the end of the workshop.

August 2021 - September 2021
Remote

AI Invasion Facilitator

Data Science Nigeria

Volunteered as a tutor for the AI Invasion program, a 5-day program to introduce participants to core concepts of Machine Learning, and hands-on practice of solving Machine Learning problems using Python. Introduced participants to Machine Learning hackathons and we participated in a 4-day Kaggle hackathon.

June 2021, May 2022
Lagos, Nigeria

Education

Software Engineer Career Progression Program

Elev8 Education | Microsoft
April 2022 - July 2022
Remote

AWS re/Start Program Learner

Lonadek Inc | AWS
September 2021 - December 2021
Lagos, Nigeria

University of Ibadan

M.SC. in Mathematics
Machine Learning with Applications in Agriculture

Abstract: Current rapid development in Artificial Intelligence (AI) provides a vast selection of high-quality tools to solve complex problems in more efficient ways than before. As a consequence, many fields of science and engineering are starting to explore AI tools, especially Deep Learning (DL) models for computer vision, audio and video understanding, speech recognition and decision making. In this project, we studied a type of deep learning model - Convolutional Neural Networks (CNNs), starting with a basic Machine Learning algorithm - Logistic Regression, moving on to building blocks of Neural Networks and method of training and optimising neural network models. We then implemented three different CNN architectures; a base model, VGG16 and ResNext-50, to classify different plant diseases using healthy and diseased leaf images. The best performing model was ResNext-50 with 98% accuracy.

2018 - 2021
Ibadan, Nigeria

Federal University of Agriculture Abeokuta

B.SC. in Mathematics
Comparison of Simplex and Revised Simplex Methods in the Solution of a Linear Programming Problem
2011 - 2015
Abeokuta, Nigeria

Skills

Programming Languages & Tools

Certifications and Awards