CDR Sample for Software Engineer (261313)

This project focused on designing, developing, and deploying a robust and scalable e-commerce platform capable of handling high user traffic and diverse product catalogues. My core responsibilities were:

I was responsible for the end-to-end development of the platform, beginning with gathering business requirements and translating them into technical specifications. I designed the microservices architecture, delineating clear boundaries for services such as user authentication, product catalogue, shopping cart, and order processing. A critical task was selecting appropriate technologies for each service, including backend frameworks (e.g., Spring Boot for Java, Node.js with Express) and frontend libraries (e.g., React.js). I led the development efforts, implemented RESTful APIs for inter-service communication, and configured message queues (e.g., Kafka) for asynchronous operations. I also oversaw the deployment process using Docker and Kubernetes, ensuring CI/CD pipelines were robust and automated. Finally, I conducted performance testing and optimisation to ensure the platform met scalability and responsiveness targets.

Skills & Technologies Demonstrated:

• Software: Java (Spring Boot), JavaScript (Node.js, React.js), Python, Docker, Kubernetes, Kafka, PostgreSQL, MongoDB, Git.
• Standards & Frameworks: RESTful API Design, Microservices Architecture, Agile/Scrum, OAuth 2.0.
• Competencies: Full-Stack Development, Distributed Systems Design, Database Management, API Development, Containerization, CI/CD, Performance Tuning, Software Architecture.

This project involved designing and implementing a mobile application that leveraged artificial intelligence for real-time data analysis and personalised user experiences. My core responsibilities were:

I was responsible for the complete lifecycle of the mobile application, from conceptualisation to deployment. I initiated by defining the application’s core features and user experience, focusing on intuitive design and efficient data processing. I then designed the application’s architecture, distinguishing between the mobile frontend (e.g., React Native, Flutter) and a cloud-based backend for AI model inference. A key challenge was integrating machine learning models for real-time data analysis (e.g., TensorFlow Lite for on-device inference or cloud-based model serving). I led the development of the mobile client, implemented features for data collection and visualisation, and ensured secure communication with the backend. I also managed the development and integration of the AI models, fine-tuning them for optimal performance and accuracy. Finally, I conducted extensive testing, including unit, integration, and user acceptance testing, to ensure the reliability and responsiveness of the system.

Skills & Technologies Demonstrated:

• Software: Python (TensorFlow, Keras), JavaScript (React Native/Flutter), Firebase/AWS Amplify, PostgreSQL, Git.
• Standards & Frameworks: Mobile Application Development (iOS/Android), RESTful APIs, Machine Learning Pipelines, UX/UI Principles, OAuth 2.0.
• Competencies: Mobile Development, Artificial Intelligence/Machine Learning, Data Science, Cloud Integration, API Development, Software Testing, UI/UX Design.

This project focused on modernising existing legacy enterprise systems by migrating them to a cloud-native, serverless architecture to improve scalability, reduce operational costs, and enhance agility. My core responsibilities were:

I was tasked with conducting a comprehensive assessment of the legacy systems to identify dependencies, bottlenecks, and opportunities for re-architecting. I designed the migration strategy, proposing a phased approach that involved re-platforming and re-factoring critical components into serverless functions (e.g., AWS Lambda, Azure Functions) and managed services (e.g., AWS RDS, Azure Cosmos DB). A crucial aspect was ensuring data integrity during migration and minimising downtime for business-critical operations. I developed new APIs for the modernised components, providing seamless integration with remaining legacy systems via API Gateways and message queues (e.g., SQS). I configured infrastructure as code (IaC) using tools like AWS CloudFormation or Terraform to automate deployment and management. Finally, I established robust monitoring and logging solutions and conducted performance and security audits post-migration to ensure optimal operation and compliance.

Skills & Technologies Demonstrated:

• Software: Python, Node.js, AWS Lambda/Azure Functions, AWS API Gateway/Azure API Management, AWS RDS/Azure SQL Database, Terraform/CloudFormation, Docker, Git.
• Standards & Frameworks: Cloud-Native Architecture, Serverless Computing, DevOps Principles, Microservices, Infrastructure as Code (IaC), Security Best Practices (IAM, Encryption).
• Competencies: Cloud Architecture, System Migration, Serverless Development, Database Management, Infrastructure as Code, DevOps, Network Security, Performance Monitoring.