• Engineered backend services using Python, Django REST Framework, and Node.js to analyze transactions, detect anomalies, and automate fraud scoring for global payment traffic.

• Built RESTful and WebSocket APIs enabling real-time fraud alerts, dispute notifications, and transaction review workflows for risk analysts.

• Integrated Stripe Radar, Experian, and LexisNexis APIs to enhance fraud models with identity verification, device fingerprinting, and behavioral analytics.

• Integrated ML-based fraud-scoring models into backend pipelines, enabling real-time risk prediction and improving high-risk detection accuracy by 14%.

• Developed asynchronous microservices using Celery, RabbitMQ, and Kafka Streams, reducing fraud-analysis latency by 35%.

• Built data pipelines for feature extraction and ML inference using Python, Kafka Streams, and PostgreSQL to support continuous model improvements.

• Implemented automated dispute and chargeback pipelines using PostgreSQL, Redis, and batch processing, improving dispute win rates by 18%.

• Designed and enforced OAuth2, RBAC, and service-level access controls to secure customer and fraud-risk data across distributed systems.

• Built internal dashboards with React.js, TypeScript, and Material UI to visualize fraud scores and trends, reducing investigation time by 30%.

• Containerized services using Docker and deployed on AWS ECS/EKS, implementing CloudWatch monitoring, S3 log archival, and anomaly alerts.

• Automated infrastructure with Terraform and optimized GitLab CI/CD pipelines for reliable multi-environment deployments.

• Developed unit, integration, and performance tests using Pytest, unittest, and Postman, achieving 95%+ test coverage.

• Collaborated with Risk Science, Compliance, SRE, and Data Engineering to optimize fraud pipelines, improving response times by 22%.

• Implemented observability with Prometheus, Grafana, and ELK Stack to detect fraud spikes, API anomalies, and ML model drift.

• Designed, developed, and owned cloud-native microservices using Python, FastAPI, and Flask to support high-scale distributed systems for internal customer, seller, and operational platforms.

• Architected and implemented event-driven systems using Apache Kafka, AWS Kinesis, and AWS SQS, processing millions of real-time events per day with low latency and high availability.

• Built distributed data processing pipelines using PySpark and Spark SQL on Amazon EMR to generate terabyte-scale feature datasets, metrics, and analytical outputs.

• Orchestrated batch and near-real-time workflows using Apache Airflow, implementing automated backfills, dependency management, failure recovery, and 99% SLA compliance.

• Designed and optimized relational and NoSQL data models using PostgreSQL and MongoDB to support transactional and semi-structured workloads, improving query performance by 30–35%.

• Developed RESTful APIs with proper versioning, authentication, and authorization, following secure API design and AWS IAM best practices.

• Built internal web applications and dashboards using React and TypeScript to enable monitoring of system health, data pipelines, and business KPIs.

• Integrated machine learning inference services into backend APIs to support personalization, optimization, and anomaly detection use cases.

• Collaborated with data science and ML engineering teams to productionize machine learning models, focusing on feature pipelines, scalable inference, and performance monitoring.

• Implemented AWS data lake architecture using Amazon S3, enabling analytics and reporting through Amazon Athena and Amazon Redshift over multi-terabyte historical datasets.

• Containerized services using Docker and deployed them on Amazon EKS (Kubernetes), implementing auto-scaling, rolling deployments, and resilient system design, reducing deployment-related incidents by 25%.