- Managed and operated Client search infrastructure that is built on top of SolrCloud
- Collaborated across AI/Client teams to analyze and optimize their search and indexing needs for different forms of data
- Diagnosed, fixed, improved, and automated complex issues across the search stack to ensure maximum uptime and performance
- Established SLA’s for all indexing and search use cases in production
- Helped write code, documentation, participated in code reviews, and mentor other engineers
- Infrastructure: Handling millions of AI/Client users (100+ PB)
SolR, Elastic Search, AWS, Kubernetes, Python, Java
Data Infrastructure Solr/Search Engineer
Technology
- SolR (Filters, Query Parsers, Performance Tuning, Group, Faceted Search).
- Information retrieval theory, (i.e. Apache Lucene/Solr/Elastic Search)
- NoSQL (Mongo), Redis
- Agile and scrum environment, working in cross functioning teams and writing and estimating user stories.
- Test Driven Development, Gherkin and Cucumber as a unit test strategy.
