• Led the design of critical infrastructure components, utilizing AWS services to enhance system reliability and scalability.• Implemented AWS Lambda functions to process notifications from SQS and seamlessly relay them to SNS, ensuring smooth communication between services.• Managed and prepared data for consumption by consumers, contributing to the seamless flow of information within the ecosystem.• Elevated code quality by adding JUnit tests using Mockito, resulting in a… Show more • Led the design of critical infrastructure components, utilizing AWS services to enhance system reliability and scalability.• Implemented AWS Lambda functions to process notifications from SQS and seamlessly relay them to SNS, ensuring smooth communication between services.• Managed and prepared data for consumption by consumers, contributing to the seamless flow of information within the ecosystem.• Elevated code quality by adding JUnit tests using Mockito, resulting in a remarkable code coverage of 90%.• Worked on optimizing the processing of messages in high-traffic services, reducing incidents of throttling during peak times. Developed strategies for managing traffic bursts effectively.• Authored comprehensive one-pagers on caching techniques to control traffic and enhance system performance, making complex concepts accessible to team members.• Increased the maximum concurrency at the SQS trigger level, providing better control over traffic for Lambda functions.• Actively participated in critical reconciliation tasks, ensuring data consistency and accuracy Show less

• Applied machine learning models (K Nearest Neighbor, Random Forest) to analyze the Chicago Crime dataset and predict culprits' arrest status, achieving 88% accuracy with Random Forest using Brewlytics for model implementation• Utilized Python for developing robust data processing scripts and optimizing algorithms• Created intuitive and responsive data visualization dashboards using Brewlytics, enhancing user experience and interface functionality• Leveraged Python data science… Show more • Applied machine learning models (K Nearest Neighbor, Random Forest) to analyze the Chicago Crime dataset and predict culprits' arrest status, achieving 88% accuracy with Random Forest using Brewlytics for model implementation• Utilized Python for developing robust data processing scripts and optimizing algorithms• Created intuitive and responsive data visualization dashboards using Brewlytics, enhancing user experience and interface functionality• Leveraged Python data science libraries such as Pandas, NumPy, and SciPy for efficient data manipulation and analysis• Implemented and optimized multi-threaded data processing algorithms in Python and C++, improving computational efficiency on Linux hosts• Conducted performance analysis and optimization of existing data processing algorithms using refactoring, multi-threading, and other advanced techniques• Worked extensively in a Linux development environment, employing various tools and libraries to streamline data analysis processes• Conducted comprehensive data analysis on the Chicago Crime dataset using Python, identifying prevalent crime types and locations• Developed a predictive system using Brewlytics to expedite culprits' apprehension, contributing to damage reduction• Provided crime prevention recommendations, demonstrating the ability to derive actionable insights from data analysis Show less

• Assisted professors in the preparation and delivery of undergraduate courses, demonstrating collaborative and support-oriented skills essential for effective teamwork.• Actively engaged in research or academic projects, showcasing strong problem-solving and analytical skills.