I'm a Software Engineer delivering AI-driven automation, full-stack platforms, and cross-functional enablement across EMEA. I thrive on rapid prototyping, clean code, and translating stakeholder feedback into production-ready features. Holding an MSc in Computer Science (Distinction expected) and a BEng in Computer Engineering, I build across MERN, Python, and AWS stacks to solve complex platform and data challenges.

ADEDAMOLA EMMANUEL ARAOYE

I'm a Software Engineer delivering AI-driven automation, full-stack platforms, and cross-functional enablement across EMEA. I thrive on rapid prototyping, clean code, and translating stakeholder feedback into production-ready features. Holding an MSc in Computer Science (Distinction expected) and a BEng in Computer Engineering, I build across MERN, Python, and AWS stacks to solve complex platform and data challenges.

Available to hire

I’m a Software Engineer delivering AI-driven automation, full-stack platforms, and cross-functional enablement across EMEA. I thrive on rapid prototyping, clean code, and translating stakeholder feedback into production-ready features.

Holding an MSc in Computer Science (Distinction expected) and a BEng in Computer Engineering, I build across MERN, Python, and AWS stacks to solve complex platform and data challenges.

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Experience Level

Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
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Language

English
Fluent

Work Experience

Software Engineer at Rasakat Investment International
October 1, 2024 - August 1, 2025
Instituted shared engineering tooling (GitHub Actions CI pipelines, automated lint/unit tests, and deployment checklists) enabling weekly releases with fewer regressions; tripled release cadence and eliminated hotfix deploys in 2025. Architected and launched the Marketplace MERN platform with secure seller onboarding, JWT-protected buyer flows, and serverless payment hand-offs between the Express backend and Vercel-hosted frontend.
Automation Engineer at Huawei Technologies
July 1, 2023 - October 1, 2024
Led end-to-end automation of regulatory and executive reporting pipelines across five business units, building Python-Selenium and VBA-based workflows that cut report preparation time by 40% and eliminated roughly 200 analyst hours per month. Engineered a microservice suite on AWS (Lambda, DynamoDB, S3) and an internal service to ingest, reconcile, and surface data quality issues in real time, increasing reporting accuracy to 99.5% ahead of quarterly audits. Coordinated cross-functional roll-out and delivered hands-on training to 30+ stakeholders, pairing knowledge bases with monitoring dashboards that reduced manual interventions by 60%.
Full Stack Web Developer at 100devs Agency
February 1, 2022 - May 1, 2023
Delivered responsive, production-grade web applications by combining modern JavaScript, Node.js, and MongoDB stacks with iterative agile workflows. Facilitated client demos, sprint reviews, and retrospectives that kept projects aligned with accessibility guidelines, SEO practices, and measurable conversion targets. Authored reusable React components and design system documentation adopted by 20+ cohort members.
Software Engineer Intern at Living Trust Mortgage Bank
October 1, 2020 - August 1, 2021
Partnered with IT to streamline daily banking operations, monitoring core banking applications, triggering incidents, and coordinating fixes to minimize service disruption. Built lightweight internal dashboards visualizing loan application KPIs, giving relationship managers same-day insight into approval pipelines. Assisted in hardening branch systems by rolling out OS patches and documenting security checks.

Education

MSc. Computer Science at Teesside University, Middlesbrough, UK
January 11, 2030 - July 1, 2026
BSc. Computer Engineering at Federal University of Technology Akure
January 11, 2030 - July 1, 2023

Qualifications

IBM AI FOUNDATIONAL COURSE
January 11, 2030 - December 4, 2025

Industry Experience

Software & Internet, Professional Services, Telecommunications
    paper Weed therapy App

    Weed Therapy is an AI-assisted wellness companion designed to support mindfulness, emotional regulation, and cannabis habit management. Users interact with an empathetic AI “therapist,” complete daily check-ins, and revisit past sessions to track progress. The system combines React, Node.js, and MongoDB with Google Gemini to deliver a therapeutic, privacy-focused experience.

    Project Link: https://www.twine.net/signin

    How It’s Built

    Tech Stack:
    React, Tailwind CSS, Node.js, Express, MongoDB, JWT Auth, Cloudinary, Google Gemini API, Web Speech API

    Frontend

    React + Tailwind for a calm, mobile-first UI

    Session-type workflows (Crisis, Daily Check-In, Reflection, Habit Builder)

    Daily check-in popup with backend state syncing

    Speech-to-text message input

    Optimized chat experience with auto-scroll, caching, and typing indicators

    Backend

    Node.js/Express REST API

    Mongoose models for users, sessions, and daily check-ins

    Secure JWT authentication with refresh-token flow

    Cloudinary for avatar/media storage

    Robust middlewares for auth, rate limiting, and validation

    AI Integration

    Google Gemini for emotional, context-aware chat responses

    Different prompt templates per session type

    Safety rules for sensitive conversations

    Core Features

    AI Therapy Chat: Adaptive responses tailored to stress, cravings, reflection, or journaling.

    Daily Check-Ins: Mood, craving, stress, and notes logged once per day.

    Session Types: Structured conversations like General Therapy, Grounding, Craving Help, Reflection, and Habit Building.

    Anonymous Mode: Sessions stored without identifiers; UI clearly marks anonymous chats.

    Session History: Authenticated users can revisit or continue previous sessions.

    Mobile App (WIP): React Native version in development.

    Key Optimizations

    Selective history fetching to reduce payload size

    Centralized session-type registry and UX-first ordering

    Per-user daily check-in logic using backend + localStorage

    Rate limits and input validation around AI endpoints

    Improved performance via lazy-loading, efficient state handling, and streamlined scrolling

    paper AI Tour Guide

    AI Tour Guide is a prototype AI-powered city exploration assistant that generates personalized, time-optimized tours using real geographic data and conversational LLM guidance.

    Features

    • Personalized Tour Planning
    • LLM-Powered Conversational Guide
    • Interactive Maps
    • Smart Routing Engine
    • State Persistence

    Tech Stack

    • Streamlit, Folium
    • Python, NetworkX, GeoPandas
    • Google Gemini API
    • OpenStreetMap

    How It Works

    1. Load a city
    2. Choose constraints
    3. Route optimization via Weighted A*
    4. Conversational AI guidance
    5. Map visualization

    Current Status

    Early WIP prototype.

    Planned Enhancements

    • Transport integration
    • Better AI prompt engineering
    • Mobile UI
    • Deployment pipeline