Srilatha Usike

About Srilatha Usike

I’m a Senior Backend Engineer specializing in cloud-native, high-scale systems, with a strong focus on search platforms and distributed architecture.Currently, I design and operate a large-scale search system handling 100M+ requests per month, owning the end-to-end search lifecycle—from data ingestion and indexing to schema design, query optimization, and relevance tuning. My work spans semantic, hybrid, and ML-driven search, delivering fast, accurate, and scalable search experiences.I bring deep expertise in building resilient backend systems using Java, Spring Boot, and Python, along with distributed technologies such as Kafka, Kubernetes, and AWS. I’ve worked extensively with search engines like Solr and Lucene, optimizing performance and relevance at scale.More recently, I’ve been exploring and building AI-powered search systems, including vector search and Retrieval-Augmented Generation (RAG), applying LLMs to improve search quality and user experience.I’m particularly interested in solving complex problems at the intersection of backend systems, search, and AI—especially in environments that demand scale, performance, and innovation.Open to connecting with professionals working on AI-driven search, distributed systems, and scalable cloud architectures.

Experience

1. Senior Application Developer

   ADP

   Jun 2022 — Present · Roseland, NJ, US

   Architected and scaled a distributed, cloud-native search platform handling 100M+ requests/month, ensuring high availability and low-latency performanceOwned the end-to-end search system, including data ingestion, indexing pipelines, schema design, query optimization, and relevance tuningMigrated legacy Java monolith applications running on Linux VMs to Spring Boot microservices, implementing CI/CD pipelines with Jenkins, containerization with Docker, and deployment on Kubernetes, improving scalability, reliability, and maintainabilityReduced query latency by ~15–30% through Lucene/Solr optimizations, caching strategies, and query design improvementsImproved search relevance and CTR by ~15–25% by implementing hybrid and semantic search, including vector-based retrieval techniquesBuilt and maintained microservices-based backend architecture using Java, Spring Boot, and Python, exposing robust REST APIs for internal and external consumptionDesigned and managed real-time data pipelines using Kafka and Zookeeper, ensuring fault-tolerant and scalable data ingestionDeployed and operated cloud-native applications on AWS (S3, Lambda, ECS, EKS), improving system scalability and reliabilityDeveloped AI-powered search capabilities using RAG, vector search, and chunking strategies to enhance semantic understanding and user experiencePerformed performance tuning and system optimization at application, search engine, and infrastructure layersWorked with MongoDB and DocumentDB for scalable storage and retrieval of large document datasetsConducted data analysis and visualization using Tableau to monitor system performance and inform relevance tuning strategiesExplored and implemented LLM-powered features to improve search quality and retrieval efficiency, including next-generation vector search and Retrieval-Augmented Generation (RAG)

Education

• Vageswari College of Engineering

  Bachelor of Technology (B.Tech.), Computer Science and Engineering

  2008 — 2012