Ganesh V

Experience

Portfolio-style experience showing product context, project ownership, and measurable outcomes.

Software Engineer — Capital One Financial

USA • Jun 2025 – Present
  • Client/Product: High-volume banking systems for real-time payments, fraud analysis, and transaction workflows.
  • Project: Real-Time Payments & AI-Assisted Fraud Analysis Platform
  • Problem: Banking workflows required low-latency transaction processing, asynchronous event handling, high availability, and better analyst support for fraud investigation.
What I Built
  • Built fintech microservices using Java, Spring Boot, and Spring Cloud to support real-time payment and transaction workflows across banking channels.
  • Migrated legacy transaction flows to an event-driven model using Apache Kafka to support asynchronous processing of transactions and notifications.
  • Built AI-assisted workflows using Python, FastAPI, RAG pipelines, vector embeddings, and LLM APIs for fraud analysis and financial document querying.
  • Deployed cloud-native services on AWS and Kubernetes and automated CI/CD using Jenkins, Docker, and Kubernetes.
Impact
  • Increased system throughput by 30% by moving transaction workflows to Kafka-based asynchronous processing.
  • Reduced API response latency by 40% through Redis caching and PostgreSQL query tuning.
  • Reduced manual investigation effort by 40% using RAG-based querying for financial documents and customer interactions.
  • Maintained 99.9% uptime for cloud-native banking applications through AWS and Kubernetes deployment patterns.
Tech: Java, Spring Boot, Spring Cloud, Python, FastAPI, Apache Kafka, React.js, TypeScript, Redux, AWS (EC2, S3, Lambda), Kubernetes (EKS), Docker, Redis, PostgreSQL, Jenkins, RAG, Vector Embeddings, LLM APIs

Research Assistant — University of North Carolina at Charlotte

Charlotte, NC • Jan 2025 – May 2025
  • Client/Product: Academic data processing, student analytics, and AI-assisted research support systems.
  • Project: Academic Analytics & RAG-Based Research Assistant Platform
  • Problem: Researchers and university teams needed faster access to insights from academic datasets, research documents, and student information without relying on manual analysis.
What I Built
  • Developed backend services using Python (FastAPI) and Java Spring Boot to support academic data processing and analytics workflows.
  • Built a Kafka-based ingestion and processing pipeline to handle high-volume academic datasets and enable near real-time reporting.
  • Developed a Generative AI assistant using RAG, LLMs, vector embeddings, and prompt handling to support intelligent querying of research papers and student data.
  • Built context-aware AI APIs to improve retrieval relevance and answer quality for academic analytics use cases.
Impact
  • Improved system reliability and modularity for academic analytics services.
  • Enabled near real-time insights and reporting for faculty and data science researchers.
  • Improved information accessibility and reduced manual research effort through RAG-based querying workflows.
Tech: Python, FastAPI, Java, Spring Boot, Apache Kafka, RAG, LLMs, Vector Embeddings, Prompt Engineering, React.js

Software Developer — Hexagon Capability Center

Hyderabad, India • Oct 2022 – Nov 2023
  • Client/Product: Geospatial and industrial analytics systems for engineering and operational monitoring teams.
  • Project: Real-Time Data Streaming & Geospatial Analytics Platform
  • Problem: Industrial monitoring platforms required low-latency streaming, reliable backend services, and interactive dashboards for operational decision-making.
What I Built
  • Built backend services using Java, Spring Boot, and REST APIs to support geospatial and industrial data processing systems.
  • Designed real-time data streaming pipelines using Apache Kafka to process industrial sensor data with sub-second latency.
  • Built Angular and TypeScript dashboards for geospatial insights and analytics visualization.
  • Integrated Node.js and Express.js APIs for service orchestration across distributed enterprise systems.
  • Implemented CI/CD pipelines and improved query performance using PostgreSQL and Redis caching.
Impact
  • Reduced data latency to near real-time (1 second) for operational monitoring workflows.
  • Improved API performance by 25% through query optimization and Redis caching.
  • Improved release efficiency and reduced manual deployment errors through CI/CD automation.
Tech: Java, Spring Boot, REST APIs, Apache Kafka, Node.js, Express.js, Angular, TypeScript, PostgreSQL, Redis, Jenkins, Git

Software Developer — CitiusTech

Hyderabad, India • Apr 2021 – Sep 2022
  • Client/Product: Healthcare applications for patient data management, clinical workflows, and healthcare analytics.
  • Project: Healthcare Application Platform & Clinical Data Workflows
  • Problem: Healthcare systems required reliable backend services, secure patient data handling, efficient integrations, and better user-facing dashboards for clinical use cases.
What I Built
  • Developed backend services using Java, Spring Boot, and REST APIs to support healthcare applications and clinical workflows.
  • Built responsive dashboards and portals using React.js and JavaScript for patient and staff-facing workflows.
  • Implemented Node.js and Express.js integrations to support data exchange across EHR/EMR systems.
  • Built performance-critical C++ modules with STL and multithreading for healthcare analytics processing.
  • Implemented database workflows using MySQL and automated build/deployment pipelines using Jenkins and Git.
Impact
  • Improved execution efficiency by 20% for performance-critical medical data workflows.
  • Improved interoperability and data consistency across healthcare integrations.
  • Reduced manual deployment effort and improved release reliability through CI/CD automation.
Tech: Java, Spring Boot, REST APIs, React.js, JavaScript, Node.js, Express.js, C++, MySQL, Jenkins, Git

Target Roles

Backend Engineer · Distributed Systems · Real-Time Platforms · AI Integration