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

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.

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.