Career Journey
Senior Full Stack Developer
Code Riffs -Cape Town
Project Link:
https://chauffeur-service-next-dwh8.vercel.app/sign-in
- Designed and implemented CI/CD pipelines for the Home Safe Chauffeur (HSC) service, automating deployment using AWS Amplify.
- Developed a Next.js frontend hosted on AWS Amplify, ensuring scalability and seamless user experience.
- Managed authentication via Clerk, secured API requests using AWS API Gateway, and leveraged Amazon S3 for file storage.
Technologies Used:
Next.js, Webflow, AWS Amplify, AWS Lambda, API Gateway, Clerk, MongoDB
Senior Full Stack Engineer
Agizo- Midrand (Remote)
Project Link:
https://main.d2aqqjvcw25hq9.amplifyapp.com/uber-pickups
Geospatial Web Application Development & Deployment:
- Developed a Next.js-based geospatial application, integrating AWS
services like Amplify Hosting, Cognito (authentication), and AppSync
(GraphQL API). - Optimized data storage and retrieval using DynamoDB and MongoDB,
improving query performance.
CI/CD & Cloud Infrastructure:
- Designed and automated CI/CD pipelines in AWS Amplify, reducing
deployment time. - Configured Amplify build settings (amplify.yml) for streamlined
deployment workflows.
Performance Optimization & System Maintenance:
- Managed deployment configurations and enhanced frontend
performance with server-side rendering (SSR). - Implemented interactive geospatial visualizations for real-time data
updates.
Technologies Used:
Next.js, React, Tailwind CSS, Node.js, Express.js, AWS AppSync, DynamoDB,
MongoDB, AWS Amplify
Junior Mobile Developer
Surion-Johannesburg
Company Website:
https://surion.anomalydev.co.za/
Cross-Platform Security Surveillance Mobile Application:
- Developed iOS and Android applications using Flutter & Dart, ensuring a seamless user experience across devices.
- Implemented ParseServer (BaaS) for authentication and backend services, improving data handling efficiency.
- Integrated MongoDB and Firebase, ensuring secure and scalable data management.
Machine Learning & IoT Security Systems:
- Built a real-time object detection system using Nvidia Jetson, YoloV5, and OpenCV (Python) to enhance surveillance capabilities.
- Configured Mosquitto MQTT Broker for IoT device communication, improving system responsiveness.
- Managed MongoDB databases to analyze and store surveillance data, enhancing threat detection.
- Utilized OpenCV and NumPy for image preprocessing, enhancing model accuracy.
- Leveraged Nvidia Jetson for edge processing, enabling real-time inference on low-latency, resource-constrained environments.
- Built a FastAPI-based REST API to handle image uploads, process requests, and return object detection results in real-time.
- Deployed YOLOv5 on Nvidia Jetson to perform low-latency object detection at the edge, reducing cloud dependency.
- Implemented MQTT messaging for efficient real-time data transfer between edge devices and the cloud backend.
Technologies Used:
YOLOv5, PyTorch, OpenCV, NumPy, FastAPI, TorchServe, PostgreSQL, MongoDB, Nvidia Jetson, MQTT