• Working on the core Gappify Accrual Cloud platform, integrating LLM-based intelligence into large-scale fintech workflows for automated invoice and accrual processing.

• Built LLM-powered document and invoice intelligence services to extract, classify, and enrich financial data consumed across distributed Accrual Cloud services.

• Architected middleware-driven integration pipelines leveraging Celigo and Versori AI to orchestrate bi-directional data flows between Gappify and enterprise ERP/P2P platforms, implementing transformation, validation, retry logic, and observability for high-volume financial workflows.

• Implemented RAG (Retrieval-Augmented Generation) pipelines combining LLMs with structured accounting and ERP data (SQL/NoSQL) to improve accuracy, grounding, and reliability in distributed systems.

• Developed AI-assisted validation, enrichment, and anomaly detection logic for financial transactions, designed for idempotency, retries, and eventual consistency.

• Designed and maintained high-throughput distributed backend services using Java, Quarkus, Vert.x, RESTful APIs, and TypeScript, optimized for low latency and scalability.

• Integrated AI and core services with AWS (Lambda, S3, SQS, EC2) using event-driven and asynchronous architectures to support fault-tolerant distributed processing.

• Secured distributed fintech workflows using OAuth2 and JWT authentication, enforcing service-to-service access control across microservices.

• Containerized and deployed services using Docker, leveraging Kafka/SQS for messaging, and CI/CD pipelines with centralized logging and observability for distributed systems.

Developed and maintained highly scalable payment processing services using Java, Spring Boot, and TypeScript, ensuring

seamless transactions for millions of customers. Built and optimized REST APIs for payment authorization, refunds, and order

cancellations, processing millions of transactions daily with AWS Lambda, DynamoDB, S3, and Kafka.

• Worked on improving the order cancellation workflow, optimizing refund accuracy and reducing processing time by 40%. Migrated

legacy synchronous flows to an event-driven architecture using SQS, Step Functions, and SNS, improving fault tolerance and

system resilience.

• Enhanced fraud detection and payment validation by integrating real-time anomaly detection with Kinesis, DynamoDB

Streams, and AWS IAM policies, reducing false positives by 25% and strengthening transaction security.

• Contributed to cost optimization efforts, reducing API latency by 30% and saving $50K annually by refactoring inefficient queries,

implementing caching strategies with Redis, ElastiCache, and optimizing S3 storage usage.

• Collaborated with finance, risk management, and product teams to enhance transaction reliability, ensuring compliance with

payment regulations. Provided technical guidance and knowledge sharing to new team members for smooth onboarding.

Implemented and deployed cloud-native applications using Python, Django, Node.js, and React.js leveraging GCP, Azure,

and Kubernetes to enhance scalability, fault tolerance, and multi-cloud compatibility.

• Built and optimized RESTful APIs with PostgreSQL, MongoDB, and implemented asynchronous processing using Rab-

bitMQ, improving data processing efficiency and reducing query response times.

• Automated CI/CD pipelines using Jenkins, GitHub Actions, and Docker, enabling seamless deployments while integrating

Terraform for infrastructure as code (IaC) and monitoring services with Prometheus and Grafana.

Engineered and optimized distributed systems infrastructure handling 400K+ daily orders, focusing on system reliability and

scalability through effective load balancing and backend optimizations using C++ and Node.js.

• Designed and implemented efficient storage solutions for large-scale database systems with MySQL, significantly improving

data retrieval speeds and backend performance for high-volume transactions.

• Built robust backend services and infrastructure leveraging Google Cloud (GCP) and Azure, integrated with scalable storage

systems using Apache Iceberg, enhancing performance and reliability of critical enterprise workloads.