Senior Software Engineer with 9+ years building large-scale distributed systems using Java, Kafka, Cassandra, Solr, Elasticsearch, Kubernetes and many others at Apple and IBM.
Led 7-engineer team across App Store, Apple Music, Podcasts, and Books to migrate the Pricing system to an event-driven indexing architecture using Kafka; reduced time-to-live for pricing updates by 40% and retired legacy synchronization workflows.
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Designed and deployed a Cassandra-backed service resolving race conditions between push-based and pull-based indexing flows; adopted as the standard pattern for all subsequent real-time indexing migrations.
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Redesigned the Media Partner Feed with custom columnar Parquet readers per consumer team via Apache Spark, replacing full-export reads with targeted column selection; reduced Spark job execution times by 75% and decoupled consumer upgrade cycles from schema changes.
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Delivered SOX compliance across the content indexing platform by adding authenticated access controls to the indexing gateway and migrating Cassandra authentication from password rotation to mTLS, eliminating credential management overhead.
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Contributed to 10+ cross-functional feature deliveries for App Store and Books Store with App Store Connect, Apple Music, and Spotlight teams covering Pricing, Offers, and Search surface areas.
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Participated in interview panels for senior/junior candidates; mentored new hires through onboarding on complex distributed systems, contributing to sustained team growth.
Designed and implemented an Elasticsearch plugin caching ML ranking models onto cluster nodes with a distributed cache-invalidation system; cut average query response time by 30%.
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Identified a cluster architecture bottleneck from proliferating small Elasticsearch cores; drove a 66% reduction in core count, meaningfully improving query throughput.
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Collaborated with ML research teams to productionize and deploy optimized ranking and NLP models into the production search stack using Java and Kubernetes-based serving infrastructure.
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Led a team of 4 interns to build a Redis-based rate limiting system for the search REST API; delivered a load/stress testing infrastructure adopted as the standard benchmarking framework across multiple product teams.
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Primary escalation point for Elasticsearch cluster reliability — diagnosed split-brain scenarios, hardened plugin deployments via rolling restarts, and handled customer escalations across Kubernetes-managed services.
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Filed patent: Cognitive Methods and Systems for Responding to System Incidents — an AI-driven automated alert triage and remediation system.
Building a system to help one of the largest banks in the United States in the task of Customer prospecting and loan prospecting using Graphical analysis of data and using machine learning models
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Using MySQL and Neo4j with Python to load data from the database and creating a graph to analyze the data and find patterns
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Involved in the process of data preprocessing, model design and implementation for the system
