• Support the design and implementation of solution architectures based on Artificial Intelligence and Generative AI. • Gather and translate business requirements into technical solutions using AI, automation and data analysis. • Develop and implement solutions using LLMs, RAG (Retrieval-Augmented Generation), AI agents and integrations with enterprise systems. • Build and expose AI models and services via APIs and microservices. • Implement solutions on cloud AI platforms, ensuring performance, security and governance. • Participate in the definition and implementation of data architectures for AI applications, including Data Lakes, Data Warehouses and vector databases. • Support proofs of concept (PoCs), technical testing and evaluation of new technologies. • Apply best practices in development, governance, security and responsible use of Artificial Intelligence. • Collaborate with multidisciplinary data, development and business teams to ensure proper integration of solutions. • Support monitoring, maintenance and continuous evolution of deployed solutions using MLOps practices. • Produce technical documentation, architecture diagrams and functional and technical specifications. • Participate in technical meetings with clients and stakeholders to understand requirements and present solutions.

Role Description As a Principal Search Architect, you will serve as the elite technical authority and visionary leader helping our largest enterprise customers unlock the full potential of Elasticsearch. Acting as a strategic, trusted advisor to CTOs and Enterprise Architecture teams, you will design, govern, and scale massive, complex Elasticsearch cluster topologies that transform application search performance, data retrieval infrastructure, and AI-powered semantic search capabilities. You will bridge the gap between business strategy and cutting-edge distributed systems engineering, collaborating directly with Elastic’s global Professional Services leadership, Core Engineering, Product Management, and Sales executives. In this high-impact role, you will shape the future of enterprise deployments by driving regional architectural standards, leading critical cluster migrations, and mentoring both Fortune 500 engineering teams and internal Elastic technical staff. What You Will Be Doing - Elasticsearch Core Architecture: Translate highly complex business requirements into resilient, next-generation enterprise retrieval architectures built natively on distributed Elasticsearch environments. - Cluster Governance & Design: 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). - Advanced Vector Search & AI Engineering: 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. - Performance Tuning & Optimization: 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. - High-Throughput Indexing: Architect robust distributed ingestion strategies handling petabyte-scale throughput, optimizing cluster state performance, thread pools, and bulk indexing requests for maximum efficiency. - Cross-Functional Influence: 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. - Community & IP Development: Capture, formalize, and publish global best practices, reference architectures, and reusable solution patterns across the broader Elasticsearch and open-source engineering communities. - Culture of Excellence: Drive internal enablement initiatives, mentor senior engineers, and cultivate a culture of continuous technical excellence and deep mastery of Elasticsearch internals. Qualifications - 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. - Education: 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. Compensation - The typical starting salary range for this role is: $159,800 — $252,800 USD. - The typical starting salary range for this role in select locations (including Seattle WA, Los Angeles CA, the San Francisco Bay Area CA, and the New York City Metro Area) is: $191,900 — $303,500 USD. Benefits - Competitive pay based on the work you do here and not your previous salary. - Health coverage for you and your family in many locations. - Ability to craft your calendar with flexible locations and schedules for many roles. - Generous number of vacation days each year. - Increase your impact - We match up to $2000 (or local currency equivalent) for financial donations and service. - Up to 40 hours each year to use toward volunteer projects you love. - Embracing parenthood with a minimum of 16 weeks of parental leave.

• Cloud architecture with a primary focus on Microsoft Azure and complementary knowledge of Oracle Cloud Infrastructure (OCI) • Responsible for designing, assessing, implementing and optimizing critical corporate cloud environments • Must have experience in infrastructure projects • Architecture, engineering and support of cloud environments, with the ability to define best practices, technical standards and recommendations for performance, availability, security, connectivity, governance and operational continuity

• Conduct performance assessments using the Dealer Maturity Model diagnostic, identifying where each rooftop sits across the five-tier progression and the specific blockers preventing advancement • Own the tier advancement plan for each account, translating DMM findings into a prioritized roadmap with clear milestones and measurable outcomes • Partner with Customer Value Managers to integrate DMM findings into Customer Value Plans and Value Review conversations • Identify and interpret industry metrics as value realization evidence • Apply structured discovery methodology to every engagement. Diagnose before prescribing, and enter each account with a defined approach, not a pre-built agenda • Frame problems in the dealer's language. Translate platform data and adoption gaps into business terms the dealer principal, GM, or department manager already uses • Navigate resistance with diagnosis, not repetition. Identify the source of pushback and adapt the engagement approach accordingly • Facilitate high-stakes conversations including value reviews, workflow redesign sessions, and change management discussions that require structured facilitation, not content delivery • Maintain current mastery of the Tekion platform across all relevant modules • Serve as the escalation resource for complex product questions that exceed CVM depth. • Design and deliver group enablement programs, including cohort sessions, virtual clinics, and workflow deep dives, that scale CVA impact beyond the 1:1 model • Contribute to CVA playbook development, turning field patterns into repeatable methodology the full team can apply