I'm Joshua Nwachinemere, an AI engineer who builds practical systems around capable models: reliable backends, controlled workflows, evaluation, and interfaces people can actually use.
My public work spans multimodal assistants, API-driven data products, ML evaluation, real-time systems, and automation. Python is my primary engineering language, especially for AI systems, backend services, data workflows, and evaluation. For product surfaces outside my core stack, I use AI-assisted development while keeping system boundaries visible, testing behavior, and documenting decisions.
I'm also building VolyxAI, exploring focused workflows for intake, validation, approval, and handoff. It is one part of my engineering work—not the whole story—and consequential actions stay with people.
A privacy-oriented macOS AI assistant for screen, voice, meeting, and coding workflows. Built with Electron, JavaScript, and native Swift components, with provider routing, consent boundaries, tests, and a deliberately inspectable architecture.
Electron JavaScript Swift AI APIs GitHub Actions
A read-only wallet analytics system combining asynchronous Python services, FastAPI, React/Vite, Telegram integration, and multiple blockchain data providers. The engineering work covers defensive API integration, portfolio data modelling, responsive product delivery, caching, and automated backend/browser verification.
Python FastAPI React Vite Telegram Docker
A real-time multiplayer game with authenticated guest sessions and server-authoritative state. The backend validates moves and rewards, handles reconnects and forfeits, and maintains a persistent two-currency economy with ledgered transactions.
React Express Socket.IO SQLite Node.js test runner
An evaluation-focused ML experiment covering automated data collection, feature engineering, XGBoost and Poisson models, chronological dataset splits, probability calibration, a FastAPI service, and a Streamlit interface. I use it to study pipeline design and honest evaluation rather than market it as a production forecasting service.
Python XGBoost scikit-learn FastAPI Streamlit SQLAlchemy
I'm open to AI Engineer and ML Engineer opportunities.
Portfolio · Email · LinkedIn · VolyxAI
- AI systems: model integration, multimodal workflows, retrieval, and structured outputs
- ML practice: evaluation design, calibration, feature engineering, and classical ML
- Backend: FastAPI, REST APIs, webhooks, async Python, and service integration
- Automation: n8n, validation, approval gates, retries, and idempotency
- Data and product delivery: SQL, SQLite, API integration, and AI-assisted web interfaces
- Shipping: Docker, GitHub Actions, Playwright, testing, and Azure