Raghavendhar K
Full Stack Software Engineer • Raleigh, NC 27560 • R*****************@gmail.com • 984****263 • drivetube.ai/•••••
Professional Summary
Full Stack Software Engineer with 4+ years of experience designing, building, and scaling cloud-native web applications and ML-driven fraud detection systems for financial services and payments. Experienced across React, TypeScript, Node.js, Java/Spring Boot, AWS, Kubernetes, Terraform, and observability platforms; delivered high-availability microservices, streaming analytics, and production ML deployments supporting large user bases.
Technical Skills
Programming Languages: Java,Python,TypeScript,JavaScript
Web Technologies: REST APIs
Frameworks and Libraries: React,Redux,Next.js,Node.js,Spring Boot,Hibernate,TensorFlow
Databases: PostgreSQL,MySQL,Oracle,Snowflake,MongoDB,Redis
Cloud and DevOps: AWS EC2, Lambda, EKS, S3, DynamoDB, IAM, RDS, Aurora, Kinesis,Azure AKS, Cosmos DB, Blob,Docker,Kubernetes,Terraform,GitHub Actions,Jenkins,Azure DevOps
Testing: OpenTelemetry,Datadog,Prometheus,Grafana,CloudWatch,Splunk,JUnit,Selenium,Cucumber,TestNG,Microservices
Data and Analytics: Kafka,AWS SQS
Skills: HTML5,CSS3
ML and AI: LLM Prompting,Fraud Detection Modeling
Work Experience
Wells Fargo
Charlotte, NC
Full Stack Engineer
Dec 2024 – Aug 2025
Built cloud-native web and backend services for Wells Fargo's financial and payments platforms, supporting web and mobile experiences for a large user base.
Tech Stack: React, TypeScript, Node.js, PostgreSQL, OpenTelemetry, Datadog, Docker, Kubernetes EKS, AWS IAM, GitHub Actions
- Developed full-stack TypeScript applications using React and Node.js with PostgreSQL, supporting 500K+ active users across web and mobile and improving feature delivery velocity.
- Implemented end-to-end observability using OpenTelemetry and Datadog, instrumenting services and LLM-specific traces to reduce mean time to detection (MTTD) by 45%.
- Led migration from a monolith to microservices on AWS EKS and Docker, increasing system uptime to 99.95% and cutting deployment time by 70%.
- Architected IAM services and secure token handling workflows using AWS IAM and industry best practices to strengthen access control and identity integrations.
- Collaborated with product, design, and data science teams to translate business requirements into scalable APIs and UI components; delivered 12+ major features on schedule following Agile processes and CI/CD.
- Improved code quality and release reliability by introducing automated tests and CI pipelines (GitHub Actions), reducing production incidents and accelerating mean time to recovery.
Visa Inc.
Austin, TX
Full Stack Engineer
Jun 2023 – Nov 2024
Worked on payment analytics and fraud detection systems for Visa's payments platform, building streaming pipelines, dashboards, and production ML services.
Tech Stack: Python, TensorFlow, LLM Prompting, Apache Kafka, AWS Lambda, Redis, React, TypeScript, Terraform, Docker, Kubernetes, GitHub Actions
- Pioneered an intelligent fraud detection solution using Python, TensorFlow, and custom LLM prompting that identified ~85% of fraudulent transactions while reducing false positives by 40%.
- Streamlined streaming data pipelines with Apache Kafka and AWS Lambda to process 500GB+ of payment analytics data daily with automatic scaling and fault tolerance.
- Modernized admin dashboards using React, TypeScript, and Redux to present real-time payment flow and system-health insights, improving operational visibility for incident response teams.
- Optimized database queries and introduced a Redis caching tier to reduce API response times by 65% and lower database load by 40%.
- Provisioned multi-cloud infrastructure with Terraform across AWS and GCP and containerized services for Kubernetes deployment to standardize environments and accelerate releases.
- Integrated ML model deployment into CI/CD using Docker and GitHub Actions to enable reproducible builds, controlled rollouts, and faster iteration of fraud models.
The Span Technologies
Hyderabad, India
Software Engineer
Jun 2019 – Jun 2021
Developed cloud-native web applications and CI/CD automation for a software product company, contributing to front-end features, backend services, and infrastructure improvements.
Tech Stack: React, Next.js, TypeScript, Spring Boot, Node.js, MongoDB, GitHub Actions, Docker, AWS S3, DynamoDB, Aurora, Azure AKS, Cosmos DB, Terraform
- Delivered full-stack features using React, Next.js, TypeScript, Spring Boot, and MongoDB across product modules, contributing to frequent user-facing releases during bi-weekly sprints.
- Maintained 90%+ unit test coverage and implemented automated UI and integration tests to increase release confidence and reduce regressions.
- Automated CI/CD pipelines with GitHub Actions and Docker, reducing release cycle time from two weeks to two days and enabling frequent deployments.
- Revamped microservices and cloud-native components using Spring Boot and Node.js, and migrated workloads to AWS and Azure services (S3, DynamoDB, Aurora, AKS), improving scalability by ~40%.
- Reduced cloud costs by 25% through Terraform-based provisioning, resource right-sizing, and improved monitoring of cloud resources.
- Partnered with QA to implement test automation and code review practices, decreasing regression defects by 30% and improving sprint velocity.
Education
SUNY Polytechnic Institute
Master of Science, Computer and Information Science • Aug 2021 – May 2023
Coursework: Algorithms and Complexity, Linux Kernel, Database Systems, Neural Networks, Machine Learning, Artificial Intelligence, Programming Languages, Operating Systems, Quantum Computing
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