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Elastic

Searches enterprise data and monitors systems

Consulting Architect - Search

Implementation & Technical Account ManagementPosted on Nov 8
Full-timeUnited States$133.1k - $252.9k

About the Job

Requirements

  • At least 5 years of experience as a Consulting Architect, Lead Engineer, or Senior Systems Consultant, with deep technical expertise focused specifically on Elasticsearch at massive scale.
  • Comprehensive understanding of distributed systems theory as applied to Elasticsearch, including consensus protocols, internal node roles, Apache Lucene indexing mechanics, and cluster state management.
  • Proven experience deploying production-grade semantic search solutions using Elasticsearch native machine-learning nodes, dense and sparse vector fields, and integrations with modern natural language processing frameworks and large language model orchestration tools.
  • Advanced knowledge of orchestrating large Elasticsearch workloads across cloud platforms such as AWS, Azure, and GCP using cloud-native patterns, Docker, and Terraform.
  • Strong proficiency in multiple programming languages, such as Java, Python, and Go, with experience using official Elasticsearch client libraries and building custom plugins.
  • A bachelor's, master's, or PhD in Computer Science, Engineering, or a related quantitative field, or equivalent deep industry experience.
  • Ability to work effectively across distributed global teams and travel to client sites as required for strategic architectural engagements.

Responsibilities

  • Translate complex business requirements into resilient enterprise retrieval architectures built on distributed Elasticsearch environments.
  • Lead technical strategy and design for customer engagements, including node blueprinting, capacity planning, custom mappings, shard strategy, Index Lifecycle Management, and cross-cluster replication and search.
  • Design and operationalize semantic search architectures using Elasticsearch vector database capabilities, including k-nearest neighbors, approximate nearest neighbors, ELSER, hybrid retrieval, and Retrieval-Augmented Generation pipelines.
  • Profile, benchmark, and tune distributed search and indexing performance for high-query-per-second environments with sub-second service-level agreements, optimizing Apache Lucene segment merging, caching layers, and heap and garbage collection configurations.
  • Architect distributed ingestion strategies for petabyte-scale throughput, optimizing cluster-state performance, thread pools, and bulk indexing requests.
  • Collaborate with Product Management and Core Engineering to influence the Elasticsearch roadmap, surface edge-case bugs, and drive feature enhancements based on enterprise customer needs.
  • Capture, formalize, and publish global best practices, reference architectures, and reusable solution patterns for Elasticsearch and open-source engineering communities.
  • Drive internal enablement initiatives, mentor senior engineers, and promote technical excellence and mastery of Elasticsearch internals.

About the Company

Elastic

Searches enterprise data and monitors systems

Elastic builds search, observability and security software for developers, IT operations teams and security analysts. Elasticsearch indexes enterprise data so applications can retrieve records, while Kibana gives teams dashboards for exploring results and investigating system activity. Customers use the tools to search business information, monitor applications and infrastructure, and investigate threats across their data. Elastic sells managed cloud services and self-managed subscriptions, with resource-based hosted pricing, usage-based serverless pricing and licenses tied to nodes and memory. Founded in 2012, Elastic lists San Francisco, California, as its headquarters.

Company size
1,001-5,000
Company stage
IPO
Headquarters
San Francisco, CA
Founded
2012

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