# Rohith Vengala > Software Engineer | Python, Java | Backend & Distributed Systems | AWS, Microservices | Ex-Amazon | Capital One | Winner at Smart India Hackathon 2020 Location: United States, United States Profile: https://flows.cv/rohithvengala Software Engineer with 4+ years of experience building scalable backend systems, distributed microservices, and cloud-native applications. Currently working on high-scale financial decisioning platforms at Capital One, previously Software Development Engineer at Amazon. Key strengths: • Backend development using Python, Java, Node.js • Cloud-native architecture using AWS (Lambda, API Gateway, DynamoDB, S3) • Distributed systems and microservices at scale • LLM-powered applications using OpenAI, LangChain, and RAG pipelines • Performance optimization and system design Selected Impact: • Reduced Amazon Seller Licensing onboarding time from 3 weeks to 3 days by redesigning the workflow using AWS Step Functions and Lambda. • Architected a multi-tenant learning platform sustaining 800+ requests per second using Django, PostgreSQL, and containerized microservices. • Built GPT-4 + LangChain RAG pipelines that reduced document analysis time by 40%. • Developed backend systems supporting large-scale financial decisioning platforms processing millions of requests daily. I enjoy designing reliable distributed systems, building scalable backend services, and integrating modern AI capabilities into production platforms. Always open to connecting with engineers, recruiters, and teams working on high-scale backend systems and cloud infrastructure. Tech Stack: Python | Java | Node.js | AWS | FastAPI | Spring Boot | Microservices | DynamoDB | PostgreSQL | Docker | Kubernetes | Spark | LangChain | RAG ## Work Experience ### Full Stack Engineer @ Capital One Jan 2025 – Present | Delaware, USA • Contributed to the development of Capital One’s Prescreen Decisioning Platform, a large-scale credit pre-approval system processing 1M+ applications daily, helping automate eligibility and risk decisions and improving approval turnaround time by 35%. • Built Python backend services using FastAPI, implementing APIs for eligibility evaluation, credit scoring orchestration, and decision storage; deployed services on AWS Lambda with API Gateway to support stateless, low-latency request handling. • Refactored decisioning workflows into containerized services using Docker and AWS Fargate, separating eligibility, scoring, and audit components to improve fault isolation and enable independent scaling during peak prescreen campaigns. • Developed Apache Spark (PySpark) jobs on Amazon EMR to process and standardize large credit bureau and transactional datasets in Amazon S3, ensuring consistent data inputs for downstream decisioning and analytics. • Integrated machine learning models hosted on AWS SageMaker into Python services for real-time credit risk scoring, contributing to an 18% improvement in model-driven decision accuracy. • Built an internal GenAI-assisted tool using Python and LangChain to generate plain-language credit decision explanations from model outputs and rule evaluations, reducing manual review and documentation effort. • Improved system performance by optimizing PostgreSQL queries and introducing Redis caching for frequently accessed eligibility data, reducing database load and lowering average API response times by ~30%. • Supported reliable deployments and production stability using Jenkins for CI, terraform for AWS infrastructure, and Datadog monitoring, helping reduce incident resolution time by 40% while maintaining PCI DSS and SOC 2 compliance ### Software Development Engineer @ Amazon Jan 2021 – Jan 2023 | Bengaluru, Karnataka, India • Led the architecture and implementation of Amazon's 3rd-Party Seller Onboarding workflows, modernizing legacy flows into scalable, event-driven pipelines using AWS Step Functions, Lambda, API Gateway, and DynamoDB — reducing onboarding time from 3 weeks ’3 days. • Migrated high-volume financial reporting pipelines (fee transactions & settlements) to modern microservices, improving data consis- tency, auditability, and operational reliability across Amazon's internal finance tools. • Improved API response latency by 25% by redesigning internal workflows, removing bottlenecks, and introducing intelligent caching layers. • Optimized the Amazon Pay Dashboard pipeline, using CloudWatch metrics, CPU/memory profiling, and infra-level debugging to improve throughput and system resilience during peak load. • Designed and executed microservice migrations from a legacy monolith, improving horizontal scalability, deployment isolation, and failure containment for high-traffic seller systems. • Built cost-efficient infrastructure blueprints and optimized compute/storage patterns, contributing to 15% AWS cost reduction across targeted services. • Automated UI workflow validations using Selenium + TestNG, integrating them into CI/CD pipelines for deployment-ready UI checks and zero-regression releases. • Enabled UI theming on the Amazon Shopping Gateway, contributing to global content campaigns and improving customer-facing content customization. • Played a key role in 24/7 on-call rotations, performing root cause analysis, resolving customer-impacting incidents, reducing MTTR, and improving system observability. ### Software Development Engineer @ kickstartX Jan 2018 – Jan 2021 | Hyderabad, Telangana, India • Architected and delivered a multi-tenant Learning Management System (LMS) as part of a larger microservice ecosystem, capable of handling 800+ RPS. Designed for high concurrency, memory-safe request handling, and autoscaling using Django, Node.js, PostgreSQL, AWS EC2, and distributed caching - reducing response latency by ~30% and enabling rapid onboarding of new enterprise clients. • Built the platform's OAuth 2.0-based authentication system with JWT + refresh tokens, improving login reliability and strengthening platform security by 3x. • Led a team of 6 engineers, owning sprint planning, code reviews, architectural decisions, and technical mentorship for interns and junior developers, improving overall delivery velocity. • Implemented automated end-to-end test suites using PyTest, TestNG, and LambdaTest for cross-browser and mobile compatibility, integrating them into CI/CD pipelines with GitHub Actions and Jenkins. • Developed core backend modules including user onboarding, course management, analytics dashboards, and RBAC permission systems, contributing to strong customer adoption. • Optimized database models, indexing strategies, and asynchronous workers, improving backend throughput by ~40% and ensuring platform stability during traffic spikes. ## Education ### Master of Science - MS in Computer and Information Systems Security/Information Assurance Wilmington University Jan 2024 – Jan 2025 ### Bachelor of Technology - BTech in Computer Science Anurag Group of Institutions Jan 2016 – Jan 2020 ## Contact & Social - LinkedIn: https://linkedin.com/in/rohithvengala --- Source: https://flows.cv/rohithvengala JSON Resume: https://flows.cv/rohithvengala/resume.json Last updated: 2026-03-23