About the company
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 precision of search and the intelligence of AI to enable everyone to accelerate the results that matter.
Responsibilities
- Translate highly complex business requirements into resilient, next-generation enterprise retrieval architectures built natively on distributed Elasticsearch environments.
- Lead the overarching technical strategy and design authority for high-stakes customer engagements—from initial node blueprinting and capacity planning to custom mappings, shard strategy, Index Lifecycle Management (ILM), and cross-cluster replication (CCR/CCS).
- Design and operationalize cutting-edge semantic search architectures utilizing Elasticsearch’s native vector database capabilities, including kNN, Approximate Nearest Neighbor (ANN), ELSER (Elastic Learned Sparse Encoder), hybrid retrieval, and Retrieval-Augmented Generation (RAG) pipelines.
- Profile, benchmark, and tune distributed search and indexing performance for ultra-high-QPS environments with aggressive sub-second SLAs, optimizing Apache Lucene segment merging, caching layers, and heap/garbage collection configurations.
- Architect robust distributed ingestion strategies handling petabyte-scale throughput, optimizing cluster state performance, thread pools, and bulk indexing requests for maximum efficiency.
- Collaborate cross-functionally with Elastic Product Management and Core Engineering to influence the Elasticsearch codebase roadmap, surface edge-case bugs, and drive feature enhancement requests based on enterprise field realities.
- Capture, formalize, and publish global best practices, reference architectures, and reusable solution patterns across the broader Elasticsearch and open-source engineering communities.
- Drive internal enablement initiatives, mentor senior engineers, and cultivate a culture of continuous technical excellence and deep mastery of Elasticsearch internals.
Requirements
- 8+ years as a Principal Architect, Lead Engineer, or Senior Systems Consultant with recognized, deep technical expertise specifically focused on Elasticsearch at a massive scale.
- Comprehensive understanding of distributed systems theory as it applies to Elasticsearch, including consensus protocols, internal node roles (master, data, ingest, machine learning), Apache Lucene indexing mechanics, and cluster state management.
- Proven track record of deploying production-grade semantic search solutions using Elasticsearch’s native ML nodes, dense/sparse vector fields, and integrations with modern NLP frameworks and LLM orchestration tools.
- Advanced knowledge of orchestrating massive Elasticsearch workloads across cloud platforms (AWS, Azure, GCP) using cloud-native patterns, Docker, and Terraform.
- Strong proficiency in multiple programming languages (e.g., Java, Python, Go) with extensive experience utilizing official Elasticsearch client libraries and building custom plugins.
- Exceptional presentation and storytelling skills, with verified experience commanding a room of executive stakeholders to align technical Elasticsearch roadmaps with core business strategy.
- Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related quantitative field, or equivalent deep industry experience.
- Comfortable working effectively across highly distributed, global teams and traveling to client sites as required for strategic architectural engagements.
Conditions
- As a distributed company, diversity drives our identity. Whether you’re looking to launch a new career or grow an existing one, Elastic is the type of company where you can balance great work with great life.
- Competitive pay base.