Content Media

At Content Media, you led the development of scalable FullStack applications, utilizing Angular for the frontend and ExpressJS for the backend to deliver high-performance user experiences. You played a key role in managing cloud infrastructure, integrating various AWS services such as S3, Lambda, EventBridge, Route 53, and CloudFront to enhance application scalability and security.

Additionally, you implemented CI/CD pipelines with GitHub Actions, significantly improving deployment efficiency and system reliability. Your contributions extended to performance optimization, leveraging serverless architectures to boost system efficiency. You also ensured high-quality code through rigorous unit testing and QA reviews, adhering to best software development practices.

Furthermore, you designed secure solutions for data management and financial transactions, strengthening system robustness.

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The project encountered severe performance bottlenecks due to an inefficient serverless architecture. The AWS Lambda functions, responsible for handling business logic, experienced frequent cold starts and unoptimized memory allocation, leading to high execution latencies. Additionally, the existing infrastructure lacked proper auto-scaling mechanisms, causing downtime during peak traffic periods.

Another critical issue was the redundant invocation of Lambda functions, increasing operational costs and degrading system performance. The architecture did not leverage caching mechanisms, forcing frequent database queries and API calls, further exacerbating response times.

Moreover, the application struggled with content delivery inefficiencies, leading to slow page loads and degraded user experience. Cloud resources were underutilized due to poor compute optimization strategies, and deployment cycles were inconsistent due to the lack of a structured CI/CD pipeline.

To address these critical issues, I implemented a series of architectural optimizations focused on improving performance, scalability, and cost efficiency.

First, I optimized AWS Lambda execution by adjusting memory and compute resource allocation dynamically based on workload requirements. I also batched API requests and introduced lazy loading techniques to minimize unnecessary function invocations, reducing cold start times and improving response rates.

To enhance scalability, I implemented AWS Auto Scaling with provisioned concurrency for critical Lambda functions. This allowed the system to pre-warm instances, drastically cutting down initialization times and ensuring high availability even during traffic surges. Additionally, CloudFront was leveraged to generate reusable Lambda templates, reducing redundant deployments and improving execution consistency.

For caching and data retrieval improvements, I integrated AWS DynamoDB with Global Secondary Indexes (GSIs) to optimize query performance and minimize direct Lambda-to-database interactions. Additionally, I incorporated Redis for caching, significantly reducing API response times by serving precomputed results.

Lastly, I implemented an automated CI/CD pipeline using GitHub Actions to standardize deployments. This streamlined code integration and ensured every update underwent rigorous testing before reaching production. Cloud monitoring tools, such as AWS CloudWatch and X-Ray, were also integrated for real-time performance tracking and debugging.

These optimizations led to a 40% improvement in application response time, a 30% reduction in operational costs, and significantly higher system availability.

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