Artist Collaboration Platform — 1M+ Users

Built the backend architecture for a global artist collaboration platform that grew to serve over 1M users. The platform enables musicians and artists worldwide to discover each other, collaborate on projects, and create music together in real time.

As Lead Backend Engineer, I owned the entire server-side architecture, from API design through deployment and scaling.

Core Platform APIs Designed and implemented the GraphQL API layer powering the entire platform — user profiles, project matching, collaboration workflows, messaging, and content delivery.

Search & Discovery Built the artist discovery engine using Elasticsearch, enabling users to find collaborators by genre, instrument, location, availability, and style. Tuned relevance scoring to surface the best matches.

AI-Powered Audio Analysis Integrated AI-based audio analysis to automatically tag and categorize uploaded tracks — genre detection, BPM analysis, key detection, and mood classification. This powered smarter recommendations and collaboration matching.

Real-Time Infrastructure Implemented Redis-backed caching and pub/sub for real-time notifications, session management, and live collaboration state. Designed the system to handle concurrent users at scale without degrading response times.

Containerized Deployment Architected the deployment pipeline using Docker containers orchestrated with Kubernetes. Built for horizontal scaling, zero-downtime deployments, and automated failover.

Scaling to 1M+ users required rethinking the data layer early. I migrated from a monolithic query pattern to read-replica MySQL with Redis caching, cutting average API response times significantly.

Audio analysis at scale meant processing thousands of uploads daily. I built an async job queue that handled audio ingestion, AI analysis, and metadata extraction without blocking the main API.

Node.js, GraphQL, MySQL, Redis, Elasticsearch, Kubernetes, Docker, AI Audio Analysis