The opportunity
Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the…
What you'll do
Elasticsearch Core Architecture: Translate business and functional requirements into highly resilient, scalable search architectures built natively on distributed Elasticsearch environments.
Cluster Design & Governance: Lead technical execution and node blueprinting for enterprise customer engagements—including capacity planning, custom mappings, shard strategy, Index Lifecycle Management (ILM), and cross-cluster replication (CCR/CCS).
Advanced Vector Search & AI Engineering: Deploy and operationalize semantic search implementations utilizing Elasticsearch’s native vector capabilities, including kNN, ELSER, hybrid retrieval, and Retrieval-Augmented Generation (RAG) pipelines.
Performance Tuning & Optimization: Profile, benchmark, and tune distributed search and indexing performance to meet demanding SLAs, optimizing JVM heap, garbage collection, caching layers, and Apache Lucene segment merging.
High-Throughput Ingestion: Build robust ingestion pipelines and bulk indexing strategies handling high-volume workloads while optimizing cluster state performance and thread pools.
Customer Engagement & Delivery: Serve as the lead technical consultant during client engagements, leading technical kickoff meetings, architectural reviews, and hands-on migration efforts.
What they're looking for
- Field Insights & Feedback: Identify edge cases, bugs, and common architectural hurdles in field deployments, sharing structured feedback with Product Management and Core Support.
- Internal Mentorship: Document implementation patterns, contribute to standard delivery frameworks, and mentor team members to cultivate a culture of technical excellence.
- + years as a Solutions Architect, Lead Engineer, or Senior Systems Consultant: with hands-on, deep technical expertise specifically focused on Elasticsearch in production environments.
- Elasticsearch Internals: Solid understanding of distributed systems principles as applied to Elasticsearch, including node roles (master, data, ingest, ML), Apache Lucene indexing mechanics, and cluster state management.
