Anthony Pafundi

Developed secure and scalable enterprise applications using Python (Django), powering music creation tools and customer platforms. Designed robust backend services for music generation and collaboration using clean architecture and various patterns. Built event-driven microservices with Python (FastAPI) and RabbitMQ for asynchronous, high-volume music data processing and real-time AI-generated music delivery. Implemented secure authentication with Django Rest Framework, OAuth2, and JWT. Developed real-time music applications using WebSockets for live collaboration. Migrated legacy systems to modern Python services, reducing technical debt. Built job processing with Celery and Redis, integrated machine learning models into backends via gRPC. Deployed GPU-accelerated music generation services using FastAPI and Docker providing low-latency REST and gRPC endpoints.

Developed distributed ETL pipelines using Python and Databricks, feeding data into reporting dashboards and AI models for song creation. Utilized Django ORM, SQLAlchemy, and PostgreSQL for relational data operations. Led CI/CD strategy with GitLab CI and Azure DevOps. Built and deployed LLM-based services for NLP tasks like lyrics generation and sentiment analysis using GPT, BERT, FLAN-T5. Fine-tuned models with PyTorch, TensorFlow, and Hugging Face, integrating AI music services into backend. Applied advanced NLP techniques such as prompt tuning and genre-specific classification. Created AI chatbots with AWS Lex and Bedrock orchestrated via FastAPI and Node.js. Extended GIS systems for music event management. Architected ML pipelines using GCP Vertex AI and Azure ML with enterprise CI/CD workflows. Spearheaded performance optimization for real-time music platforms.

Developed secure and scalable enterprise applications using Python and Django, powering music creation tools and customer-facing platforms. Designed robust backend services for music generation and collaboration using clean architecture principles. Built event-driven microservices with FastAPI and RabbitMQ for asynchronous high-volume music processing and real-time AI-generated music delivery. Implemented secure user authentication with OAuth2 and JWT. Developed real-time music applications with WebSockets enabling live collaboration. Migrated legacy systems to modern Python-based services, reducing technical debt. Built job processing using Celery and Redis. Integrated ML models via gRPC. Deployed GPU-accelerated music generation services providing low-latency REST and gRPC endpoints.