Hamza Waheed

Owned Python engineering across production services supporting large-scale image processing and AI-driven product workflows. Designed and evolved FastAPI microservices with clear separation of API handling, asynchronous processing, and data ingestion responsibilities to improve service isolation, maintainability, and horizontal scalability. Optimized async request/processing paths, caching, and database access to reduce end-to-end processing latency by ~25% under high-volume load. Built Kafka-driven event and data processing pipelines using Redis and PostgreSQL, improving pipeline throughput by ~20%. Strengthened production quality through pytest-based unit and integration testing, structured logging, monitoring, mocked dependencies, and strong failure recovery for API interactions and async/AIfacing integrations. Provided technical leadership by defining engineering standards, conducting system and code reviews, mentoring engineers, and coordinating delivery across Python services, da

Led Python engineering across production services supporting large-scale image-processing and AI-driven product workflows. Owned system architecture across APIs, asynchronous execution, and event-driven processing, ensuring scalable, observable, and maintainable high-volume Python systems with real-time AI functionality. Designed and evolved FastAPI microservices by separating API handling, async processing, and data ingestion responsibilities to improve isolation and horizontal scalability. Optimized asyncio-based request and processing workflows with caching and database access path improvements, reducing end-to-end processing latency by 25% under high load. Built Kafka-driven event and data pipelines using Redis and PostgreSQL, improving pipeline throughput by 20%. Strengthened production quality using pytest-based unit and integration testing, structured logging, monitoring, mocks, and robust failure recovery across async workers and AI integrations. Provided technical direction by

Led Python engineering across production services supporting large-scale image processing and AI-driven product workflows. Owned API and asynchronous execution architecture with event-driven processing, database access, service integrations, testing strategy, and production operations. Designed FastAPI microservices separating API handling, async processing, and data ingestion responsibilities to improve isolation, maintainability, and horizontal scalability. Optimized asyncio-based request/processing flows using caching and database access path improvements to reduce end-to-end latency by ~25% under high volume. Built Kafka-driven event and data-processing pipelines using Redis and PostgreSQL to improve pipeline throughput by ~20%. Strengthened production quality with Pytest unit/integration testing, structured logging, monitoring, mocked dependencies, and failure recovery across APIs and async workers, including AI-facing service integrations. Defined engineering standards, reviewed