• Designed and shipped Spotify's centralized safety backend service for human-agent interactions, hosting ML models via vLLM with high-throughput, low-latency serving across Spotify's agentic experiences

• Built catalog-scale AI inference data pipelines processing Spotify's full podcast and audiobook catalog, integrating ML inference as a core pipeline step for automated safety classification at production scale

• Designed and built the multi-modal video inference pipeline supporting Spotify's video podcast catalog end-to-end — from ingestion through model serving to downstream consumers

• Optimized model hosting infrastructure for cost, throughput, and latency — applying batching, GPU utilization tuning, and serving-layer improvements to vLLM-based deployments

• Owned backend service design, API contracts, observability, and reliability for AI-powered systems serving production traffic

• Partnered with ML, Trust & Safety, and Platform teams to ship reliable AI inference systems integrated into Spotify's broader service architecture

• Tech: Java, Python, vLLM, model serving, distributed systems, data pipelines, GCP, Docker, Kubernetes, microservices

Successfully completed Wayfair Full-Stack Labs program.

Currently working in Wayfair Storefront B2B team.

While still working full time on feature work, was able to join the Wayfair labs program catered specifically for the new IOS engineers at the company and learned Swift.

Worked on a variety of projects spanning from purely backend work in PHP and SQL to purely Front- end work in React.js and Swift 4.

Responsible for the technical design of dozens of projects both for the Desktop, Mobile (Full stack) and iOS platforms.