All work projects — internal Amazon links omitted.
Three-tier local lookup (Bloom Filter → RocksDB → Caffeine cache) added to a metrics ingestion pipeline handling 5.8M batches/sec and 10T+ metrics/day. Sub-microsecond rejection at 99% of incoming traffic.
AI-powered NL interface to device telemetry data spanning Redshift, DynamoDB, and Athena. Reduced metric investigation from multi-day support threads to minutes via self-service queries.
Org-wide automated code review using LangGraph orchestration with 7 parallel specialized reviewers and multi-stage filtering. Cut average time-to-review and time-to-merge by 65%.
Multi-stage AI pipeline (triage → design → implement → QA → review) that takes a ticket and produces a merged code review. Adopted org-wide; ~80% reduction in routine task time.
Shared enrichment library that lets device telemetry schemas update at runtime, dropping dimension-update time from months (next OTA cycle) to 15 minutes — zero device-side changes.
Cross-platform consent engine across FireOS, AOSP 14/15, and a new Amazon OS. Write-once-deploy-everywhere artifact generation with custom Soong build steps and boot-time constraint workarounds. Primary consent layer on 200M+ Fire TV devices.