Madhu K
Senior Software Engineer @NI (National Instruments)
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WORK HISTORY
Senior Software Engineer @NI (National Instruments)
I’m excited to share that I’m currently working on an important project focused on migrating legacy WebLogic code to a modernized Spring Boot architecture. This initiative is a significant step toward enhancing our application’s performance and maintainability.Current Focus:Modernization: We are refactoring the existing WebLogic codebase to Spring Boot, adopting microservices architecture principles to improve scalability and agility.Kubernetes Deployment: Our goal is to deploy the application on Kubernetes, ensuring seamless scalability and efficient resource management.CI/CD Enhancements: We’re implementing robust CI/CD pipelines to automate testing and deployment, which will ultimately reduce time-to-market and improve release cycles.This ongoing project presents exciting challenges and opportunities, and I’m grateful to be part of a dedicated team working together to innovate and deliver high-quality solutions.I look forward to sharing more updates as we progress! If you’re interested in discussing legacy migrations or Kubernetes deployments, feel free to reach out!#SpringBoot #Kubernetes #Microservices #SoftwareDevelopment #DevOps #LegacyMigration #TechInnovation
ABOUT MADHU K
Full-Stack Developer with 10 years of experience building and scaling web applications across finance, cloud services, and AI-enabled platforms. Adept at developing microservices using Java, Spring Boot, and creating responsive, component-driven UIs using ReactJS, Angular, and TypeScript. Proficient in designing RESTful APIs, implementing event-driven architectures using Kafka and RabbitMQ, and deploying applications in cloud environments (AWS – EC2, S3, Lambda, RDS; Azure). Experienced with Docker for containerization and CI/CD pipelines using Jenkins and GitHub Actions for automated delivery. Skilled in integrating AI/ML features and LLMs into enterprise software applications. Developed microservices that interact with LLMs such as GPT-3.5, GPT-4, and fine-tuned BERT models to deliver features like semantic search, intelligent code suggestions, contextual recommendations, and natural language-based automation. Applied prompt engineering, few-shot learning, and API-based orchestration to embed language understanding into internal developer tools and client-facing platforms. Built reusable front-end components using Material UI, enforced accessibility and performance best practices, and implemented test automation strategies using JUnit, Mockito, and Cypress. Passionate about writing clean, scalable, and secure code, collaborating with cross-functional teams, and continuously enhancing developer velocity. Actively use AI coding assistants like GitHub Copilot and ChatGPT to refactor code, accelerate feature development, and experiment with innovative solutions using LLM APIs.
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