Naheemot Adebiyi

NA

Open to opportunities

Naheemot Adebiyi

@naheemotadebiyi

AI & Machine Learning Engineer building scalable and predictive systems.

What I'm looking for

I’m looking to build real-world, production-grade AI—especially RAG and LLM-driven systems—with strong engineering mentorship. I want to work on measurable, high-impact products where data, models, and reasoning come together end-to-end.

I’m an AI and Machine Learning Engineer with hands-on experience building intelligent, context-aware systems and predictive models. I focus on end-to-end AI solution development—from data preprocessing and feature engineering to implementing Retrieval-Augmented Generation (RAG) pipelines and multi-step reasoning workflows.

In my Generative AI Fellowship (Aug 2025 – Mar 2026), I built production-grade generative AI systems using LLMs, RAG architectures, and agent-based reasoning. I strengthened my practical expertise with LangChain, LangGraph, prompt engineering, vector databases, and conversational AI design.

Highlights include a Drug-Herb Interaction Checker for Nigeria and sub-Saharan Africa, where I built an NLP pipeline using BioBERT/PubMedBERT, curated a PostgreSQL evidence-graded dataset (200+ herb-drug pairs), and deployed a web app delivering real-time interaction alerts. Earlier, as an AI/ML Trainee, I delivered supervised learning projects like a Telecom Complaint Resolution Prediction System and a Breast Cancer Detection System—bringing models from data cleaning to deployment-ready workflows.

Experience

Work history, roles, and key accomplishments

GP

AI Engineer Fellow

Generative AI Fellowship Program

Aug 2025 - Mar 2026 (7 months)

Built production-grade generative AI systems using LLMs, RAG architectures, and agent-based reasoning workflows. Developed DHIC, a drug–herb interaction clinical decision support tool with a 200+ pair PostgreSQL/Neo4j knowledge graph and evidence-graded severity alerts, and implemented a multi-step RAG chatbot using LangChain/LangGraph.

LLMs (LangChain LangGraph Retrieval Augmented Generation (RAG)Prompt Engineering LLM fastAPI PostgreSQL Neo4j

MP

AI/ML Engineering Trainee

3MTT — 3 Million Technical Talent Program

Mar 2025 - Dec 2025 (9 months)

Trained on applied supervised machine learning, including end-to-end data preprocessing, feature engineering, model training/evaluation, and deployment-ready workflows. Built a telecom complaint resolution risk classifier and a breast cancer benign-vs-malignant detection model using scikit-learn.

Python scikit learn Supervised Learning Classification Feature Engineering data preprocessing Model Evaluation

Education

Degrees, certifications, and relevant coursework

UH

University of Port Harcourt

Bachelor of Science, Biochemistry

Earned a Bachelor of Science in Biochemistry from the University of Port Harcourt, graduating in 2017.

Tech stack

Software and tools used professionally

GitHub

Jupyter

PostgreSQL

Gmail

Neo4j

F#

scikit-learn

FastAPI

LangChain

ChromaDB

Port

Evidence

LangGraph

Dynamic

X++

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