• Developed a Kubernetes application to automatically scale and optimize resource allocation through advanced algorithms and feedback loops, driving cost savings and improving deployment efficiency by an average of 27%.• Created D3 charts to visualize cost and resource utilization data, offering cluster right-sizing recommendations to help Kubernetes users make informed decisions and optimize cluster efficiency.• Engineered Kubernetes event-driven auto-scaling with multiple metrics triggers and custom scaling policies based on application criticality and workload variability, resulting in a 15% increase in scaling responsiveness.• Utilized the Kubernetes client library to programmatically refine deployment specs, ensuring optimal resource allocation and preventing under/over-provisioning, resulting in enhanced cluster performance and efficiency.• Boosted application performance by implementing in-memory data storage with Redis to persist user preferences, reducing database queries, and decreasing resource optimization operations and their latency by 21%.• Applied Next.js for its built-in server-side rendering to boost application performance, reduce debugging and development time with fast refresh, and enhance codebase organization using its pages router.• Constructed multi-architecture (ARM, AMD) and multi-OS (Linux, Windows) Docker images, ensuring flexibility and facilitating consistent deployments across various systems and cloud environments.• Conducted performance testing using K6 to simulate traffic on Google Kubernetes Engine (GKE) deployed dummy applications, validating the dynamic scaling capabilities of the Kubernetes operator to ensure system responsiveness.• Packaged Kubernetes YAML configurations into a Helm chart, enabling easy cloud deployment of services, roles, and dependencies, while allowing dynamic application customization via the values file.• Product developed under tech accelerator OS Labs (opensourcelabs.io).

• Implemented Redux Toolkit for efficient state management and database interactions, leveraging its capabilities such as async thunk, to reduce code complexity and ensure scalable, maintainable access to user financial information.• Employed Express.js to create robust server routing, enabling easy middleware implementation to streamline complex tax calculations and efficiently manage state tax rates, exemptions, and filing statuses, to ensure application scalability.• Optimized UI using Tailwind CSS's utility-first approach to streamline development, reducing CSS bloat and enabling rapid prototyping for a scalable and visually appealing design.• Integrated JWT for secure authentication, leveraging its stateless and self-contained structure to provide a seamless interaction for application users to access their tax information.

• Leveraged React’s component-based architecture, virtual DOM, and React Router to create a modular, responsive UI, enhancing user experience with minimized network requests and conditional rendering of user listening insights.• Architected the application's back-end with Node.js to capitalize on its asynchronous, non-blocking, event-driven nature, enhancing server response times and efficiently handling concurrent requests for improved application responsiveness.• Enhanced data retrieval speeds and application scalability by deploying PostgreSQL, implementing strategic indexing and caching techniques tailored specifically for optimizing user listening history, which directly resulted in faster query responses and improved handling of user load.