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Full Stack Developer

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About

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Here is a little background

Software Engineer with 4+ years of experience building and supporting backend and platform systems in production environments. Previously worked at JPMorgan Chase and Lowe’s on Java and Spring Boot microservices, Kafka-based event-driven systems, and AWS infrastructure, with a focus on system reliability and performance. Currently based in the United States and open to full-time Software Engineer / Backend / Platform roles.

Experience

Software Engineer

JPMorgan Chase

Jan 2022 – Dec 2023

• Architected a real-time event streaming and analytics system for transactional monitoring and reporting across financial workflows.
• Engineered Kafka-based pipelines with partitioned topics, idempotent consumers, and PostgreSQL-backed REST services for aggregation and querying.
• Scaled throughput to 500K–1M events/day while sustaining sub-200ms end-to-end latency and zero data loss under failure simulations.
• Strengthened reliability by tuning partitions, optimizing consumer concurrency, and implementing offset-based replay strategies.
• Accelerated incident detection by 40% through end-to-end observability using Prometheus and Grafana during load testing.

Software Engineer

Lamar University

Jul 2024 – Present

• Built an internal web-based platform to manage research data and publications using Django and PostgreSQL, reducing manual data handling and operational overhead.
• Deployed and maintained services on AWS with monitoring and uptime ownership, ensuring reliable access for faculty and staff users.
• Collaborated with cross-functional teams including faculty and admin staff to streamline content workflows, enhancing website update turnaround by 25%.
• Coordinated with backend systems (submission portals and archives) to ensure smooth data flow into front-end displays, bridging the gap between static content and dynamic workflows.
• Led departmental initiatives to improve user experience and site usability based on faculty feedback, aligning UI/UX enhancements with institutional goals.

Java Developer

Lowe's

Jan 2021 – Dec 2021

• Modernized legacy billing and customer account services supporting order processing, invoicing, and account management.
• Developed scalable backend services using Java, Spring Boot, Hibernate, and REST APIs integrating billing, order, and inventory modules.
• Boosted performance by 25% faster page loads and 40% improved query efficiency through SQL optimization across MySQL and PostgreSQL.
• Reduced manual release effort by 50% by introducing Dockerized CI/CD pipelines with Jenkins.
• Improved customer workflows by refining React and Bootstrap UI components, lowering navigation errors by 25%.
• Increased production stability through structured logging, monitoring, debugging and automated testing with minimal regressions.

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Projects

Traveler Review Analyzer - Clustering for Recommendation Insights. image

Case Study 1 of 3: Traveler Review Analyzer - Clustering for Recommendation Insights.

Applied unsupervised machine learning techniques to cluster traveler reviews and enhance recommendation accuracy in travel platforms. Designed a system to analyze 1,000+ travel reviews and group users based on behavior and preferences. Used Python, Pandas, Scikit-learn, and Matplotlib to apply K-Means and DBSCAN clustering on the BuddyMove dataset. Performed dimensionality reduction and location-based trend analysis. Improved content personalization by 25%, uncovered geospatial behavior trends, and provided a data-driven basis for travel recommendations.

Intrusion Detection System - ML Powered Cybersecurity Model image

Case Study 2 of 3: Intrusion Detection System - ML Powered Cybersecurity Model

Built a machine learning–based intrusion detection system to identify network anomalies and potential cyberattacks. Designed and trained models using Random Forest and applied Principal Component Analysis (PCA) for dimensionality reduction and improved feature selection. Evaluated on benchmark datasets with custom preprocessing pipelines in Python. Achieved 92% detection accuracy and reduced false alarm rates by 25%, improving the system’s precision and reliability. The project was formally published as part of undergraduate research and contributed to enhanced real-time network threat analysis.

Netflix 2.0 - A React-Powered Streaming Platform image

Case Study 3 of 3: Netflix 2.0 - A React-Powered Streaming Platform

Built a feature-rich streaming web app mimicking Netflix’s core UI and functionality with user authentication and real-time database storage. Developed a Netflix-style video streaming platform with personalized content browsing and secure login/logout. Implemented with React, Redux, and Firebase (Firestore + Google Auth). Data was stored and retrieved in real time, with UI responsiveness achieved using Tailwind and conditional rendering. Reached 10K+ active users, increased engagement with personalized content delivery, and achieved a 20% uplift in session time through secure and smooth navigation.

Contact

Code, Ideas, or Coffee — I’m in. Let’s connect.

+1 337 602 8469

bandathmakuru.rushi@gmail.com

United States