• Utilized encapsulation to hide implementation details, promoting data integrity and code abstraction.• Developed an enterprise application for a client in the e-commerce industry using Python with front-end using HTML, CSS, JavaScript with data stored in MySQL hosted on Amazon S3.• Implemented Django web framework and connected to a PostgreSQL database hosted on a local server gather data from third-party sources and process it along with the internal data.• Implemented data processing and analysis using Python libraries such as NumPy, resulting in faster and more efficient data- driven decision making for machine learning algorithms.• Utilized AWS services such as Lambda, API Gateway, and DynamoDB to build serverless applications, resulting in efficient and low-maintenance solutions.• Utilized Kafka Connect and Kafka Streams for seamless integration with external data sources and data processing pipelines.• Successfully designed and deployed highly available and scalable EC2 instances to meet specific application requirements, ensuring optimal performance and resource utilization.• Implemented auto-scaling groups on EC2 instances to automatically adjust compute capacity based on traffic patterns, resulting in improved application responsiveness and cost optimization.• Implemented AWS Glue jobs using serverless computing technologies such as AWS Lambda and AWS S3 reducing operational costs and improving scalability for Python-based ETL workflows.• Designed and implemented CI/CD pipelines using tools like Jenkins or GitLab CI for automated build, testing, and deployment processes.• Utilized containerization technologies like Docker to ensure portability and scalability of microservices.• Developed data processing pipelines using Python and related frameworks, ensuring scalability, performance, and reliability for handling multimedia data.