Monitored Kafka payment event streams in real-time, reacting instantly to failed transactions, cancellations, and disputes, improving event reliability to 98% and reducing processing delays by 20%.

Automated retry logic and background jobs with Celery and RabbitMQ, ensuring payment retries, client notifications, and financial operations ran smoothly, increasing retry success by 15%.

Built real-time analytics pipelines with Spark, PySpark, Kafka Streams, and Redis, generating live KPIs like transaction success rates,

Managed PostgreSQL for transactional data and MongoDB for dynamic client metadata, updating rules and discount configurations in response to promotions or changing business requirements.

Designed and maintained RESTful and GraphQL APIs (FastAPI, Ariadne) for internal dashboards, operational tools, and client portals,

Created interactive dashboards using React.js, TypeScript, Chart.js, and Material UI to visualize failed payments, refunds, revenue alerts, and transaction trends, allowing operations teams to detect and resolve issues in real-time.

Developed microservices and backend tools in Python and Node.js for event validation, financial exports, and automated reconciliation, improving day-to-day operational efficiency.

Packaged services with Docker, deployed on AWS EKS using Helm charts and blue-green deployments, while managing serverless

Built modular microservices for user segmentation, ad scoring, personalized recommendations, and revenue tracking, exposing secure REST APIs for internal analytics and ad operations teams.

Implemented low-latency, event-driven pipelines with Apache Kafka to stream user clicks, ad impressions, session events, and engagement metrics in real-time, powering instant predictions and content ranking.

Created interactive dashboards with React.js, TypeScript, Redux, HTML5, and CSS3 to visualize live engagement trends, revenue anomalies, and priority alerts, enabling faster decision-making for ad optimization and campaign adjustments.

Developed machine learning models using scikit-learn, TensorFlow, and Pandas for real-time engagement prediction, churn analysis, and personalized content ranking, improving ad targeting accuracy by 20% and user retention by 15%.

Managed data storage and retrieval with PostgreSQL for structured data, MongoDB for unstructured logs, and Redis for caching highdemand predictions, reducing dashboard load times by 35%.

Built asynchronous ML inference pipelines using FastAPI and asyncio to handle thousands of prediction requests per second, ensuring reliability during peak traffic.

Developed anomaly and fraud detection services to identify unusual engagement patterns, click spamming, and bot activity, reducing false positives by 25% and strengthening revenue integrity.

Deployed services on AWS (EC2, S3, Lambda, RDS), containerized with Docker, and orchestrated using Kubernetes (EKS) for high availability, auto-scaling production workloads.

Automated CI/CD pipelines using GitHub Actions, Helm, and Terraform for continuous integration and secure deployments across development.