• Improved model prediction consistency by 32% by unifying batch and real-time feature pipelines using Python-based transformation frameworks, ensuring reliable feature parity across training and inference systems

• Reduced feature serving latency by 41% by designing low-latency APIs with caching strategies and optimized data retrieval pipelines, enabling faster fraud detection and real-time decision-making systems

• Increased pipeline efficiency by 28% by implementing scalable streaming and batch processing workflows, reducing data processing delays and improving feature freshness for high-volume transaction environments

• Developed scalable backend services using Python, FastAPI, and microservices architecture, enabling efficient feature computation, API orchestration, and seamless integration across distributed machine learning systems

• Engineered real-time and batch data pipelines using Apache Flink, Spark, and Airflow, ensuring high-throughput processing, fault tolerance, and consistent feature availability across production-grade distributed environments

• Optimized system performance through caching strategies, asynchronous processing, and efficient data access patterns, demonstrating strong problem-solving skills, ownership, and ability to handle complex distributed systems challenges

• Collaborated with ML engineers, data scientists, and product teams to translate ambiguous requirements into scalable solutions, demonstrating strong communication, leadership, and stakeholder alignment skills

• Applied modern development practices including CI/CD pipelines, Docker, Kubernetes, and automated testing, ensuring reliable deployments, scalability, and maintainability of backend services in cloud-native production environments on AWS

• Leveraged AWS services including S3, EC2, Lambda, and managed data systems, deploying scalable microservices and data pipelines while ensuring high availability, fault tolerance, and secure infrastructure management practices

• Supported real-time content processing pipelines by assisting in Python-based API integrations and data workflows, improving moderation response time by 18% across high-volume multilingual streaming environments.

• Assisted in integrating machine learning inference services into backend systems, enabling faster harmful content detection and reducing manual review workload by 22% through efficient data routing pipelines.

• Contributed to internal review tools and backend APIs, improving content reviewer productivity by 15% through enhanced data visibility, structured moderation queues, and optimized user interaction workflows.

• Collaborated with cross-functional teams to translate requirements into scalable backend solutions, demonstrating strong ownership, communication, and problem-solving.

• Worked with Python, REST APIs, Kafka, and Spark to support real-time data pipelines, asynchronous processing, and distributed systems, strengthening backend engineering and data processing fundamentals at scale.

• Gained hands-on experience with microservices architecture, event-driven systems, and caching strategies, contributing to performance optimization and improving system reliability in high-throughput production-like environments.

• Assisted in integrating frontend components using React and GraphQL APIs, enabling efficient data visualization and improving usability of internal tools for monitoring, debugging, and operational workflows.

• Gained hands-on experience with Docker, Kubernetes, and CI/CD pipelines on Azure cloud, supporting containerized deployments, automated testing workflows, and improving release efficiency in cloud-native distributed systems.

• Supported backend services, monitoring workflows, and scalable API deployments on Azure cloud using Python and microservices architecture, enhancing observability, reliability, and high-availability distributed application performance.