Principal Search Consulting Architect

Elastic Elastic · Enterprise · Canada · Consulting - AMER

Principal Search Architect role focused on designing and scaling complex Elasticsearch clusters for enterprise customers, with a strong emphasis on AI-powered semantic search, vector databases, and RAG pipelines. The role involves deep technical expertise in Elasticsearch internals, distributed systems, and cloud-native infrastructure, collaborating with engineering and product teams to influence the roadmap.

What you'd actually do

  1. Translate highly complex business requirements into resilient, next-generation enterprise retrieval architectures built natively on distributed Elasticsearch environments.
  2. 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).
  3. 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.
  4. 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.
  5. Architect robust distributed ingestion strategies handling petabyte-scale throughput, optimizing cluster state performance, thread pools, and bulk indexing requests for maximum efficiency.

Skills

Required

  • 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.
  • Elasticsearch Internals Mastery: 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.
  • AI-Powered Search Expertise: 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.
  • Cloud-Native Infrastructure: Advanced knowledge of orchestrating massive Elasticsearch workloads across cloud platforms (AWS, Azure, GCP) using cloud-native patterns, Docker, and Terraform.
  • Polyglot Coding: Strong proficiency in multiple programming languages (e.g., Java, Python, Go) with extensive experience utilizing official Elasticsearch client libraries and building custom plugins.
  • Elite Communication: Exceptional presentation and storytelling skills, with verified experience commanding a room of executive stakeholders to align technical Elasticsearch roadmaps with core business strategy.

Nice to have

  • Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related quantitative field, or equivalent deep industry experience.
  • Global Mindset: Comfortable working effectively across highly distributed, global teams and traveling to client sites as required for strategic architectural engagements.

What the JD emphasized

  • recognized, deep technical expertise specifically focused on Elasticsearch at a massive scale
  • Comprehensive understanding of distributed systems theory as it applies to Elasticsearch
  • 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.

Other signals

  • Elasticsearch
  • AI
  • Search
  • Large Enterprise Customers
  • Distributed Systems