AWS Certified AI Practitioner
Amazon Web Services
I'm Chinemeze Njoku — a fullstack software engineer and AI/ML engineer based in Lagos, Nigeria. My work sits at the intersection of production backend systems and applications built around Large Language Models.
I'm specialized in Python, FastAPI, PostgreSQL and React, with hands-on experience building REST APIs, prompt engineering pipelines, retrieval-augmented generation (RAG) systems and vector-backed memory architectures. I'm comfortable owning projects end to end — from data pipeline and model design through deployment on cloud platforms.
As a Python Backend Intern at IntelliVerse, I engineered and deployed AI-driven API features and data pipelines into production mobile applications, and worked closely with the mobile team to diagnose and resolve critical backend endpoint failures. Alongside that, I've taught a structured Python curriculum at Stable Genius Academy since May 2025 — project-based learning and code reviews that sharpen the same debugging instincts I rely on in production work.
Certifications
2026
2025
Elements of AI
University of Helsinki
2025
Python for Data Science
IBM
2025
Python Essentials 1
Cisco Networking Academy
How the toolbox is used
Capabilities in the context of the work they serve.
On the AI/ML side, I integrate LLMs — Gemini and OpenAI — behind prompt engineering pipelines and retrieval-augmented generation systems, using pgvector and other vector databases for long-term memory, and Scikit-Learn and PyTorch where a project calls for classical or deep learning. Agentic systems and multi-step reasoning pipelines are a recurring thread across my projects.
The backend layer is built on Python — FastAPI most often, Flask or Node.js/Express where a project calls for it — with REST API design, PostgreSQL and Redis handling persistence and session state. On the frontend, React and Next.js with TypeScript and Tailwind CSS turn those APIs into interfaces people actually use.
Everything ships through AWS, Vercel, Render and Neon, with Git/GitHub and CI/CD keeping the path from commit to deployment short. Jupyter Notebook and Google Colab round out the toolbox for the modeling and experimentation side of the work.