Nikhil Raja Kottur

Collaborate closely with product managers and designers to build end-to-end features that improve deal tracking, data visibility, and workflow efficiency. • Design and develop scalable backend services using Java and Python, exposing well-structured REST APIs to support real-time application workflows. • Build responsive, user-focused frontend components using React and TypeScript, ensuring seamless user experiences across devices. • Own features across the full stack, proactively troubleshooting issues in production and iterating quickly to deliver customer value. • Optimize application performance, reliability, and data integrity through efficient system design and backend optimization.

-Developed AI-powered payment processing solutions, improving security and transaction speed. -Designed RESTful APIs using Java, Spring Boot, and React, enhancing system integration. -Implemented CI/CD pipelines with Jenkins and GitHub Actions, reducing deployment time.

- Developed and deployed full-stack applications using Java, Spring Boot, and React as part of coursework and collaborative projects. - Designed and implemented microservices architecture to improve scalability and modularity for software systems. - Utilized cloud platforms like AWS (EC2, S3, Lambda) and Azure for deploying and managing applications. - Gained hands-on experience in database management using MongoDB, MySQL, and PostgreSQL to optimize data storage and retrieval. - Collaborated in Agile teams to deliver high-quality software, employing tools like JIRA, Jenkins, and Docker for CI/CD and efficient project management. - Enhanced skills in front-end development with React, HTML, CSS, and Bootstrap, focusing on creating responsive and user-friendly interfaces.

-Architected and deployed distributed systems using Java, Spring Boot, and Kubernetes, ensuring 99.9% system uptime. – Redesigned microservices architecture to enhance scalability and modularity, reducing deployment times by 40%. – Developed secure and scalable RESTful APIs, facilitating seamless integration with internal and external systems. – Implemented advanced caching strategies and optimized database queries, reducing query response times by 60%. – Built and managed containerized applications with Docker and orchestrated workloads using Kubernetes. – Set up automated monitoring and alerting with Prometheus and Grafana, decreasing mean time to detect issues by 50%. – Designed and maintained CI/CD pipelines using Jenkins and GitHub Actions, streamlining deployment processes. – Collaborated with cross-functional teams to integrate DevOps best practices, enhancing development efficiency and operational stability

- Enhanced payment processing efficiency by 30% through optimized API response times and database query tuning in Spring Boot. - Developed and integrated secure Payment API Gateways, reducing transaction failures by 40%. - Designed and implemented RESTful APIs for payments, refunds, and transactions, achieving a 25% decrease in response latency. - Ensured 100% compliance with PCI-DSS standards, implementing AES encryption and tokenization for secure transactions. - Integrated OAuth 2.0 and JWT-based authentication, improving payment security and reducing unauthorized access by 35%. - Built microservices for payment processing, reducing server downtime by 50% using Spring Cloud and Kubernetes. - Optimized transaction logging with Kafka and Elasticsearch, reducing error resolution time by 60%. - Automated daily reconciliation processes, reducing financial discrepancies by 90%. - Developed and integrated webhooks for real-time payment status updates, enhancing user experience and reducing disputes by 25%. - Improved transaction throughput by 35% through caching strategies using Redis and database optimizations. - Revamped frontend payment interfaces using React.js, Redux and Material UI, boosting UI responsiveness by 50%. - Automated CI/CD pipelines (Jenkins, GitHub Actions), reducing deployment time by 60% and improving release efficiency.

Designed end-to-end machine learning pipelines, handling data preprocessing, feature engineering, and model deployment. Deployed AI-driven applications on AWS, optimizing resource allocation and reducing costs by 25%.