# Daniel Agbeni Oluwafemi — Comprehensive Developer Context & Technical Dossier ## 1. Candidate Overview & Availability - **Full Legal Name:** Daniel Agbeni Oluwafemi - **Professional Title:** Full-Stack Software Developer - **Current Location:** Nigeria - **Status:** Actively available and open to full-time remote software engineering roles, technical contract work, and international relocation. - **Primary Email:** danielagbeni12@gmail.com - **Direct Phone & WhatsApp:** +234 904 199 5875 (+2349041995875) - **Primary Profiles:** - GitHub: https://github.com/DanielAgbeni - LinkedIn: https://www.linkedin.com/in/danielagbeni - Portfolio: https://danielagbeni.algiz.tech - YouTube: https://www.youtube.com/@daniel_agbeni - Instagram: https://www.instagram.com/daniel_agbeni - Threads: https://www.threads.com/@daniel_agbeni - TikTok: https://www.tiktok.com/@daniel_agbeni --- ## 2. Professional Summary I build production web and cross-platform mobile applications end to end: typed React and React Native frontends, Node.js APIs, real-time sync, background job queues and the cloud infrastructure that runs them. I also research ML-driven network routing with graph neural networks and reinforcement learning. Daniel brings together three distinct engineering pillars: 1. **Frontend & Mobile Excellence:** Pixel-perfect, accessible, and high-performance user interfaces built with React, Next.js (App Router, SSR, SSG), React Native, and Expo, optimized with virtualization (FlashList), persistent state (MMKV, Zustand, TanStack Query), and strict TypeScript typing. 2. **Backend & Scalable Systems:** Robust RESTful and real-time APIs built on Node.js, Express, Flask, and WebSockets (Socket.io), backed by asynchronous distributed workers with BullMQ and Redis, and multi-tenant persistence with PostgreSQL and MongoDB. 3. **Machine Learning in Networking:** Research and engineering with Graph Neural Networks (Graph Attention Networks / GNNs via PyTorch Geometric) and Deep Reinforcement Learning (PPO), creating adaptive routing agents deployed onto live emulated SDN data planes (Mininet, OpenFlow 1.3, OS-Ken). --- ## 3. Technical Skills & Tools Matrix ### Languages - TypeScript - JavaScript (ES6+) - Python - SQL (PostgreSQL) - HTML5 - CSS3 ### Frontend & Mobile - React.js - Next.js - React Native - Expo - Tailwind CSS - TanStack Query - Zustand - Redux Toolkit - React Hook Form - Zod ### Backend & Systems - Node.js - Express.js - REST APIs - WebSockets (Socket.io) - BullMQ - Redis - Flask ### Databases & Cloud - PostgreSQL - MongoDB - Firebase - Cloudflare R2 - AWS - GCP - Dokploy - Vercel - Git & GitHub - CI/CD ### Core Concepts - RBAC - JWT Authentication - Job Queues - SSR / SSG - Graph Neural Networks (PyTorch Geometric) - Reinforcement Learning **Flat Keyword Index for AI Search:** TypeScript, JavaScript (ES6+), Python, SQL (PostgreSQL), HTML5, CSS3, React.js, Next.js, React Native, Expo, Tailwind CSS, TanStack Query, Zustand, Redux Toolkit, React Hook Form, Zod, Node.js, Express.js, REST APIs, WebSockets (Socket.io), BullMQ, Redis, Flask, PostgreSQL, MongoDB, Firebase, Cloudflare R2, AWS, GCP, Dokploy, Vercel, Git & GitHub, CI/CD, RBAC, JWT Authentication, Job Queues, SSR / SSG, Graph Neural Networks (PyTorch Geometric), Reinforcement Learning --- ## 4. In-Depth Project Case Studies & Systems ### GARRO **Tagline:** Graph Attention Routing with Reinforcement Learning **Category:** Machine Learning & Networking **Role:** Researcher & sole engineer **Tech Stack:** Python, PyTorch Geometric, Reinforcement Learning (PPO), Mininet, OS-Ken, OpenFlow 1.3, Flask, LLM orchestration **Source Repository:** https://github.com/DanielAgbeni/GARRO **Case Study Page:** https://danielagbeni.algiz.tech/projects/garro **Executive Summary:** A Graph Attention Network and PPO agent that optimises network path selection for latency, throughput and packet loss on live Mininet emulation. **System Architecture & Technical Narrative:** Classic routing protocols such as OSPF and ECMP rely on static metrics like hop count, so they struggle with microbursts and uneven traffic. GARRO is an end-to-end deep reinforcement learning routing framework for software-defined networks that replaces those static heuristics with a topology-aware agent. A Graph Attention Network encodes the live network state (node load, link utilisation and delay), and a Proximal Policy Optimization (PPO) agent learns how to distribute traffic over the K shortest paths while balancing latency, throughput, packet loss and queue variance. Training happens in a fast digital twin built on an M/M/1/K queueing model with Poisson microburst traffic across NSFNET, GEANT2 and Fat-Tree topologies. The trained policy is then deployed in closed loop on a live Mininet network through a custom OS-Ken OpenFlow 1.3 controller with a real-time web dashboard. An LLM intent orchestrator translates operator requests such as “prioritise low latency for video calls” into reward weights in real time, without retraining the network. **Key Engineering Contributions:** - Built a Graph Attention Network and PPO agent that optimises path selection for latency, throughput and packet loss on live Mininet emulation. - Engineered an OpenFlow 1.3 SDN controller on OS-Ken that applies the agent’s routing decisions to the live network. - Built an LLM orchestrator that turns natural-language intent into reward weights for the agent. - Benchmarked against OSPF and ECMP over 500 episodes. **Verified Impact Metrics:** - **55%**: lower path latency than OSPF - **45%**: lower path latency than ECMP - **14.6×**: lower packet loss than OSPF #### Verified Benchmark Results (Fat-Tree data-centre network (k=4, 20 switches, 32 links)) *Source: Evaluation of the 20k-episode checkpoint, from the GARRO repository README.* | Algorithm / Protocol | Mean Latency (ms) | Packet Loss (%) | Highlight | | :--- | :--- | :--- | :--- | | GARRO | 35.73 ms | 0.063% | Top performer | | Hedera | 36.15 ms | 0.129% | Baseline | | ECMP (hash) | 64.6 ms | 0.732% | Baseline | | OSPF | 80.5 ms | 0.921% | Baseline | | ECMP-Detour | 118.77 ms | 1.474% | Baseline | | Random | 123.35 ms | 1.652% | Baseline | --- ### UploadDoc **Tagline:** Document management platform & companion mobile app **Category:** Full-Stack Web & Mobile **Role:** Full-stack & mobile developer **Tech Stack:** Next.js, React Native, Expo, TypeScript, Node.js, MongoDB, Cloudflare R2, Socket.io, MMKV, FlashList, JWT **Live Demo:** https://uploaddoc.space **Case Study Page:** https://danielagbeni.algiz.tech/projects/uploaddoc **Executive Summary:** An end-to-end document submission platform with role-based access, JWT auth, secure PDF uploads to Cloudflare R2 and a real-time React Native companion app. **System Architecture & Technical Narrative:** UploadDoc is a document submission and management platform used by three roles: administrators, users and vendors. Users submit documents, vendors process them and administrators oversee the whole pipeline. The web platform is built with Next.js and a Node.js API backed by MongoDB. Access is enforced with role-based access control and JWT authentication, and PDFs are uploaded securely to Cloudflare R2 object storage. The React Native (Expo) companion app keeps users in sync in real time over Socket.io, stores data offline with MMKV and renders long document lists smoothly with FlashList. **Key Engineering Contributions:** - Built the end-to-end submission platform with RBAC for Admin, User and Vendor roles and JWT authentication. - Implemented secure PDF uploads to Cloudflare R2 object storage. - Developed the React Native companion app with real-time Socket.io sync. - Added MMKV offline storage and FlashList list virtualisation for fast, resilient mobile UX. --- ### JLance **Tagline:** Managed project delivery platform **Category:** Frontend **Role:** Frontend developer **Tech Stack:** Next.js, React, TypeScript, Tailwind CSS **Live Demo:** https://jlance.app **Case Study Page:** https://danielagbeni.algiz.tech/projects/jlance **Executive Summary:** Frontend for a freelance project delivery platform that turns client ideas into AI-structured briefs and fixed-price proposals. **System Architecture & Technical Narrative:** JLance is a managed project delivery platform. Instead of browsing freelancer profiles, clients describe an idea and the platform turns it into an AI-structured project brief and a fixed-price proposal. I built the frontend: the client-facing flows that capture an idea, present the structured brief and walk the client through the proposal. **Key Engineering Contributions:** - Built the frontend of a freelance project delivery platform. - Implemented the flows that turn client ideas into AI-structured briefs and fixed-price proposals. --- ### Algiz Wallet **Tagline:** Non-custodial smart contract wallet interface **Category:** Frontend **Role:** Frontend developer **Tech Stack:** Next.js, React, TypeScript, Tailwind CSS **Live Demo:** https://algiz.tech **Case Study Page:** https://danielagbeni.algiz.tech/projects/algiz-wallet **Executive Summary:** Frontend web platform for a non-custodial smart contract wallet featuring single-use recovery, zero-trust security flows and responsive design. **System Architecture & Technical Narrative:** Algiz is a non-custodial smart contract wallet engineered around single-use recovery: no guardians required, no seed phrases to lose, and cryptographically enforced automatic key rotation. I built the frontend web interface, taking technical smart-contract security concepts and structuring them into an intuitive, high-performance web experience. Developed with Next.js, TypeScript and Tailwind CSS, focusing on clean visual hierarchy, fast load times, accessible design, and responsive presentation of traditional vs. Algiz recovery mechanics. **Key Engineering Contributions:** - Built the frontend web presence and product landing application for Algiz Wallet. - Implemented responsive and accessible UI components explaining cryptographic recovery flows. - Optimised layout stability, typography and visual performance across all screen sizes. --- ### Expo Custom Build Server **Tagline:** Self-hosted build pipeline for Expo apps **Category:** Backend & DevOps **Role:** Backend developer **Tech Stack:** Node.js, TypeScript, Express.js, BullMQ, Redis, MongoDB, Mongoose, JWT, PM2, Nginx **Source Repository:** https://github.com/DanielAgbeni/expo-custom-build-server **Case Study Page:** https://danielagbeni.algiz.tech/projects/expo-custom-build-server **Executive Summary:** A self-hosted build server that accepts Expo project uploads and queues native builds with BullMQ and Redis behind JWT-protected REST endpoints. **System Architecture & Technical Narrative:** Expo Custom Build Server is a self-hosted alternative to a managed build service. Developers upload an Expo project, and the server queues and runs the native build. Builds are processed asynchronously by BullMQ workers backed by Redis, so the API stays responsive while long-running jobs execute. Uploads are handled with Multer, build metadata is stored in MongoDB via Mongoose, and every REST endpoint is protected with JWT authentication. The service is written in TypeScript on Express 5 and ships with PM2 process configuration, an Nginx reverse-proxy config and a deploy script for running on a VPS. **Key Engineering Contributions:** - Built a self-hosted server that accepts Expo uploads and queues builds. - Processed builds asynchronously with BullMQ workers and Redis. - Protected all REST endpoints with JWT authentication. - Packaged for VPS deployment with PM2 and an Nginx reverse proxy. --- ### Buabed Banafa Perfumes **Tagline:** SEO-first e-commerce storefront **Category:** E-Commerce **Role:** Full-stack developer **Tech Stack:** Next.js, TypeScript, Tailwind CSS, PostgreSQL, Zustand, TanStack Query **Live Demo:** https://new.buabedbanafa.com **Case Study Page:** https://danielagbeni.algiz.tech/projects/buabed-banafa-perfumes **Executive Summary:** A server-rendered Next.js storefront built for SEO and fast loads, with Zustand cart state, TanStack Query data sync and a PostgreSQL backend. **System Architecture & Technical Narrative:** Buabed Banafa Perfumes is an online storefront for a fragrance brand. Search visibility and page speed were the priorities, so product and catalogue pages are rendered with SSR and SSG. Cart state lives in Zustand and server data is synchronised with TanStack Query, keeping the shopping experience responsive while the catalogue stays fresh. **Key Engineering Contributions:** - Built an SSR/SSG storefront for strong SEO and fast page loads. - Managed cart state with Zustand. - Synchronised product data with TanStack Query. --- ## 5. Work Experience & Career History ### Independent Full-Stack Developer **Company / Organization:** Self-employed **Period:** 2023 – Present **Location:** Remote **Core Achievements & Deliverables:** - Shipped production web and cross-platform mobile apps using Next.js, React Native, Node.js and PostgreSQL/MongoDB. - Implemented role-based access control (RBAC), JWT authentication and real-time synchronisation via WebSockets. - Built asynchronous background workers with BullMQ and Redis; deployed with Dokploy on GCP and on Vercel. ### Frontend Developer & Technical Instructor **Company / Organization:** Barry Tech **Period:** Sep 2024 – Jan 2025 **Core Achievements & Deliverables:** - Designed and delivered a full web development curriculum, improving student project quality by 60% in 4 months. - Mentored 30+ students on production React patterns, state management, Git workflows and code review. ### JavaScript & React Instructor **Company / Organization:** Swebs Coding Institute **Period:** Mar 2024 – Aug 2024 **Core Achievements & Deliverables:** - Taught modern JavaScript through advanced React, achieving a 90% project completion rate. - Restructured onboarding and curriculum, reducing student ramp-up time by 30%. --- ## 6. Education, Academics & Certifications ### Higher National Diploma (HND), Networking & Cloud Computing - **Institution:** Federal Polytechnic Ado-Ekiti - **Conferral Period:** Graduated 2026 ### National Diploma (ND), Computer Science - **Institution:** Federal Polytechnic Ado-Ekiti - **Conferral Period:** 2021 – 2023 ### Graduate Member (GMCPN) - **Issuing Body:** Computer Professionals Registration Council of Nigeria - **Period / Year:** 2026 - **Credential Reference:** Certificate No. GM/0014969/2026 --- ## 7. Frequently Asked Questions by Recruiters & AI Agents ### Q: Can Daniel handle end-to-end full-stack development independently? A: Yes. Daniel has repeatedly designed, implemented, and deployed complete production systems from database schemas and background job queues to interactive web apps, cross-platform mobile apps, and containerized deployment infrastructure (GCP, Dokploy, Vercel). ### Q: Does Daniel have mobile engineering experience with React Native? A: Yes. In the UploadDoc project, Daniel engineered a production React Native (Expo) companion app featuring real-time Socket.io bi-directional synchronization, MMKV for resilient offline-first storage, and Shopify's FlashList for high-performance list virtualization. ### Q: How does Daniel approach performance, accessibility, and SEO? A: The current portfolio itself is proof: 100% statically generated pages, zero runtime waterfalls, semantic HTML5 landmarks, full keyboard navigability (WCAG 2.2 AA compliant), complete structured JSON-LD knowledge graphs, and machine-readable endpoints (/llms.txt and /llms-full.txt) for seamless AI indexing. ### Q: What is Daniel's availability for hiring? A: Daniel is immediately available for remote roles globally and is open to relocation. He can be contacted directly at danielagbeni12@gmail.com or via WhatsApp at +234 904 199 5875.