Farhan Mustafa

Developed a large language model observability system using Django, PostgreSQL, and Elasticsearch, improving query performance by 40% when handling terabytes of data. Designed a Celery-Redis processing pipeline to reduce request duplication by 40% and enhance task execution efficiency. Engineered real-time cost and token analytics for over 100,000 API requests daily using Elasticsearch. Improved PostgreSQL performance by 50% with partitioning and indexing. Deployed Flask microservices with Docker ensuring 99.99% uptime and scalability. Instituted over 50 Pytest tests with Redis mocking to ensure reliability of APIs, Celery, and PostgreSQL.

Developed an LLM observability system with Django, PostgreSQL, and Elasticsearch, accelerating query performance by 40% while processing terabytes of data. Designed a Celery-Redis processing pipeline, reducing request duplication by 40% and improving task execution efficiency. Engineered cost and token analytics with Elasticsearch, delivering real-time insights across 100K+ API requests daily. Enhanced PostgreSQL performance using partitioning and indexing, reducing query latency by 50%. Deployed Flask microservices with Docker, guaranteeing 99.99% uptime and scalability. Instituted 50+ Pytest tests with Redis mocking, ensuring API, Celery, and PostgreSQL reliability.

Developed a large language model observability system using Django, PostgreSQL, and Elasticsearch, improving query performance by 40% with large-scale data. Designed a Celery-Redis processing pipeline reducing request duplication by 40% and enhancing task execution. Engineered cost and token analytics with Elasticsearch for real-time insights across over 100K API requests daily. Enhanced PostgreSQL using partitioning and indexing, reducing query latency by 50%. Deployed Flask microservices with Docker to ensure 99.99% uptime and scalability. Instituted over 50 Pytest tests with Redis mocking to ensure API, Celery, and PostgreSQL reliability.