Design, build, and maintain Solr-based search platforms

Optimize search relevancy, ranking, and query performance

Build and manage indexing pipelines for large datasets

Deploy and operate services in AWS

Collaborate with cross-functional teams on search and recommendation features

Explore and implement ML-driven enhancements for search

Define and implement hybrid search strategies combining keyword and vector-based retrieval

Build and operate vector search pipelines within Solr/Lucene

Measure and improve search relevance using evaluation metrics and experimentation

Maintain Solr in production (SolrCloud), including collections, sharding, replication, and scaling

Establish monitoring, SLOs, runbooks, and participate in incident response and root cause analysis

Strong experience with Apache Solr and backend development (Java or similar)

Experience working with AWS

Solid understanding of search architecture, indexing, and performance optimization

Experience designing and tuning search relevance strategies

Hands-on experience with SolrCloud in production environments

Familiarity with vector search and modern retrieval techniques

Experience with machine learning in search or recommendation systems

Familiarity with Elasticsearch or OpenSearch

Experience with distributed systems and large-scale data processing

Exposure to embedding-based retrieval and approximate nearest neighbor (ANN) algorithms

Experience with re-ranking or learning-to-rank approaches