Full Stack Developer at InnoPay Technologies

• Designed and maintained Java Spring Boot microservices for payment processing, invoicing, and reconciliation, handling 1,500–2,000 transactions per minute during peak business hours. • Designed and maintained Java Spring Boot microservices for payment processing, invoicing, and reconciliation, handling 1,500–2,000 transactions per minute during peak hours. • Implemented event-driven architecture with Apache Kafka to decouple services (payment → invoice → ledger), reducing end-to-end processing latency by ~35% and improving fault tolerance. • Developed high-throughput microservices in Golang for payment fraud detection and reconciliation, leveraging Go concurrency to increase processing speed by ~25%. • Built responsive role-based dashboards using React and Angular integrated with backend APIs, improving merchant onboarding efficiency and reducing manual effort by ~30%. • Automated AWS infrastructure provisioning (VPC, EC2/EKS, RDS, Cognito, S3) and application deployments using Terraform, Docker, Jenkins, and EKS, reducing setup and deployment time by ~60% and enabling frequent zero-downtime releases. • Integrated message-driven architectures using Azure Service Bus and Apache Kafka for asynchronous communication between microservices, enhancing system reliability and decoupling components. • Tuned backend services and databases (Java, Node.js, SQL/NoSQL) for performance, improving average response times by ~20% through caching, query optimization, and resource adjustments. • Integrated NPCI UPI payment flows and GST e-invoicing APIs into the platform, achieving >99.5% invoice reconciliation accuracy across multi-tenant financial transactions. • Implemented secure authentication and authorization using AWS Cognito, OAuth 2.0, and JWT, enforcing RBAC and supporting over 10,000 concurrent user sessions with minimal latency. • Built and maintained CI/CD pipelines with Jenkins and Helm, automating build, testing, Dockerization, and Kubernetes deployment of microservices for continuous delivery. • Enhanced system reliability by integrating comprehensive monitoring (AWS CloudWatch, ELK Stack) and incident response runbooks, achieving 99.9% uptime and reducing MTTR by ~40%. • Integrated RabbitMQ for asynchronous event processing in the invoice reconciliation workflow, decoupling services and improving resilience under peak loads. Tech: Java 17/21, Spring Boot, Apache Kafka, React, Angular, Node.js, Python, AWS (EKS, EC2, S3, RDS, Cognito), Jenkins, Terraform, Docker, PostgreSQL, MySQL, MongoDB, Oracle Impact: Scaled platform to support 400K–1M users annually, delivering secure, auditable, and low-latency UPI and GST-compliant payment workflows.

IT Developer at BNP Paribas

• Designed and developed Java Spring Boot services processing 5–10 million options positions daily for regulatory reporting, ensuring deterministic calculations and 100% accuracy during FINRA and OCC audits. • Implemented position aggregation, multi-leg options strategy handling, and rule-based validations aligned with FINRA Rule 2360A, eliminating downstream reconciliation discrepancies and audit exceptions. • Created Python-based data ingestion pipelines to validate and transform trade and risk data, reducing manual reconciliation efforts by ~40% and accelerating daily regulatory reporting by ~30%. • Integrated Apache Kafka messaging for streaming trade event data into analytics systems, decoupling components and improving processing resilience and scalability. • Built and enhanced Angular-based internal web applications (CM² Client Risk Portal, Risk Position Explorer, Trade Reconciliation Console), improving compliance data visibility and reducing investigation time by ~25–30%. • Developed secure CM² Client Risk Evaluation modules, including React/Angular forms, role-based views, and REST API integrations to centralize onboarding, document verification, and periodic client reviews. • Integrated Elasticsearch-backed search and Kibana dashboards within CM², enabling rapid client lookup, audit-log visibility, and reducing issue triage time for compliance teams by ~30%. • Automated reconciliation checks and regulatory data validation using Python and shell scripts, reducing manual effort by ~40% and accelerating daily reporting cycles by ~30%. • Led Oracle database migration from 12c to 19c across environments, tuning schemas and queries to improve performance by ~20–25% with zero production incidents. • Developed scalable data pipelines on Azure (Data Factory, Functions) with Python and PySpark, enabling near-real-time analytics and automating ETL processes for client data. • Created AI-powered tools using Python and LangChain for document processing and Q&A capabilities, integrating Azure Cognitive Search and OpenAI APIs to build intelligent chatbots and assistants. • Developed backend validation services to normalize high-volume position data into canonical schemas. • Automated data anomaly detection using Python, integrating explainable alerts into CI pipelines. • Designed regulatory data ingestion flows suitable for enterprise data lake and CosmosDB architectures. • Implemented application telemetry and logging to support analytics and anomaly detection. • Built Python pipelines to transform LMS export data into relational reporting schemas. • Acted as a key technical contributor during regulatory and compliance reviews, explaining system architecture, data lineage, internal controls, and monitoring mechanisms to auditors and risk stakeholders. Tech: Java 8/17, Spring Boot, Angular, React, REST APIs, Oracle (12c/19c), Elasticsearch, Kibana, Autosys, Python, Shell Scripting, J