Search & AI Discovery Architect

Heritage Auctions•Irving, TX

8h•Onsite

About The Position

Heritage Auctions is seeking a Search & AI Discovery Architect to make its information easier for people and AI systems to discover, understand, trust, and cite across traditional search engines, AI-powered search, answer engines, assistants, and emerging agent ecosystems. The position combines search expertise with hands-on technical architecture and experimentation. The architect owns technical designs, information and relationship models, standards, feasibility assessments, dependencies, implementation guidance, and validation supporting Heritage’s search and AI-discovery strategy. The role builds prototypes and reference implementations, then partners with Engineering to translate validated approaches into production capabilities. As part of the Enterprise AI team, the position reports to the Manager, Enterprise AI and works closely with the SEO and AEO Strategy Manager, who owns commercial strategy, growth priorities, the delivery backlog, and business outcomes. The architect advises and influences the growth roadmap through technical evidence and emerging capabilities. The goal is to make Heritage a more accessible and trusted source for auctions, collectibles, valuations, provenance, historical objects, and market intelligence while preserving the meaning and accuracy of its information. In partnership with the SEO and AEO Strategy Manager, the role supports improved visibility and citations for strategic topics and a measurable contribution to qualified traffic, registrations, consignments, bidding activity, purchases, and revenue. The role is located south of Dallas/Fort Worth International Airport, at the northwest corner of W. Airport Freeway (Hwy. 183) and Valley View Lane. Compensation is commensurate with experience.

Requirements

Experience in technical SEO, web architecture, digital marketing technology, organic growth, or a related discipline, with strong knowledge of crawlability, indexing, website structure, metadata, canonicalization, and search performance.
Broad technical understanding of front-end websites, back-end systems, structured data, APIs, data flows, and AI-discovery integrations.
Ability to build and test prototypes and reference implementations and translate them into practical implementation guidance.
Ability to model entities and relationships clearly and preserve the meaning of complex information across systems.
Familiarity with AI-powered search, large language models, answer engines, and emerging discovery practices.
Ability to translate business and marketing objectives into technical requirements and explain capabilities, constraints, and tradeoffs to technical and nontechnical partners.
Experience partnering with developers, data engineers, product or project managers, marketers, and business stakeholders.
Strong analytical, research, communication, and problem-solving skills, including judgment to evaluate unfamiliar technologies and recommend a practical path forward.
Ownership of technical outcomes, including proactively identifying opportunities, surfacing risks, resolving gaps, and following work through validation.

Nice To Haves

Experience implementing or guiding schema, structured-data, knowledge-graph, or entity-modeling initiatives.
Familiarity with answer engine optimization (AEO), generative engine optimization (GEO), entity authority, or LLM visibility measurement.
Working knowledge of JavaScript-rendered websites, content management systems, APIs, data feeds, and modern web architecture.
Familiarity with agent integrations, MCP, or other methods for making organizational content available to external AI systems.
Experience with Google Analytics, Google Search Console, Semrush, Ahrefs, Adobe Analytics, Salesforce, Robohead, or similar platforms.
Experience with marketplaces, ecommerce, luxury goods, auctions, collectibles, or other expertise-driven industries.

Responsibilities

Define, document, and govern technical standards for crawlability, indexing, rendering, canonical URLs, metadata, internal linking, structured data, content architecture, and search performance; maintain reusable implementation patterns.
Assess how Heritage’s inventory, archives, specialist expertise, and market data are exposed to search engines and AI systems, and identify barriers to accurate discovery, retrieval, attribution, and citation.
Develop information models, reusable schema patterns, and knowledge and relationship graphs that distinguish objects from auction appearances, active offerings from historical auction results, and estimates from realized prices.
Preserve provenance, relevant dates, price definitions, and reliable links between records; work with Heritage specialists, Data Services, and Engineering to validate entity definitions, attributes, identifiers, and relationships.
Validate that discovery outputs preserve important information distinctions.
Translate business priorities into technical designs, requirements, dependencies, tradeoffs, and acceptance criteria for Product and Project Management, Web, Data Services, and Engineering.
Provide practical implementation guidance and test cases, surface risks and unresolved dependencies early, and help resolve technical gaps through production delivery and validation.
Build prototypes and reference implementations using Heritage data to test technical approaches, expose assumptions, and demonstrate proposed standards; run focused experiments, compare results, and iterate before recommending broader adoption.
Evaluate APIs, data feeds, agent access, Model Context Protocol (MCP) capabilities, and emerging discovery approaches, providing practical guidance on security, data availability, feasibility, and integration.
Track evolving schema practices, naming conventions, and changes in how search engines and AI agents discover and interpret information; use experimental evidence to explain implications and surface capabilities and constraints that should inform planning while following agreed business priorities.
Establish technical and discovery baselines with the SEO and AEO Strategy Manager and relevant analytics partners; measure and validate improvements in information accuracy, crawlability, index coverage, structured-data quality, and accessibility to search and AI agents.
Collaborate on measurement of visibility, citations, qualified traffic, conversions, and agreed business outcomes, distinguishing technical performance from commercial results.
During the first 90 days, establish discovery, technical-accessibility, and information-accuracy baselines for an agreed representative scope; identify consequential barriers and dependencies; agree on a representative category’s information model and pilot acceptance criteria; build and test a reference implementation; deliver and validate a measurable implemented pilot with Engineering; and document results, limitations, and recommendations for subsequent business-prioritized improvements.