AI Transformation Engineer at Yext in New York, NY

Job description

Yext (NYSE: YEXT) is the enterprise agentic marketing platform. Built on the world's most comprehensive structured data platform for local businesses, Yext gives brands and their partners the visibility intelligence to win every moment of discovery – across AI and traditional search. Yext's API-first architecture connects structured data to APIs, MCP servers, and generative interfaces, so partners and developers can build purpose-built experiences on the same infrastructure powering Yext's own products. Thousands of brands and digital marketing partners in financial services, healthcare, retail, hospitality, and food rely on Yext to manage, measure, and optimize visibility at scale. For more information, visit yext.com . The AI Transformation Office partners with departments across Yext to improve business outcomes through 30 to 60 day transformation sprints. As an AI Transformation Engineer , you build the production systems that make that real: retrieval, answer drafting, routing and evaluation on live data, plus the APIs, automations and pipelines around them. Each sprint ends with a handoff to the team that owns the outcome. This is not a product-roadmap engineering role. You embed with department teams, learn how work actually gets done, and build with whatever fits. That is usually a workflow platform such as n8n first, and code only where the platform runs out. You care as much about whether someone else can run your work as whether it runs in production. You build and you teach, as one motion. What You'll Do Production LLM systems (40%) Build and run production LLM workflows in n8n and similar platforms: retrieval over ticket and documentation data, answer drafting and routing. Build APIs, integrations and data pipelines that connect CRM, support and operational systems. Build and calibrate confidence scoring so teams know when to trust an answer, and design the review steps and approval gates where a person decides. Protect the data you work with: scope what each workflow can read, handle PII and sensitive fields, and keep access logs. Ship every solution monitored, error-handled and version-controlled, not just functional. Evals and quality (15%) Define evals before each build: test sets, groundedness and accuracy checks, and the thresholds that decide whether output ships or needs review. Report quality results to partner teams in plain terms. Track LLM spend and usage by team, manage API keys, and report run rate. Knowledge transfer and enablement (20%) Pair-build with embedded team members so they learn by doing, not by watching. Write documentation that scales: runbooks, admin guides, architecture diagrams and decision logs. Train department champions at the right altitude: enough to maintain and extend, not enough to overwhelm. Build with handoff in mind from day one. The owning team should run each system day to day within 30 days of launch. They own workflow configuration, prompts, review queues and reports. You keep the retrieval, eval and scoring code until a second engineer can take it. System design and architecture (15%) Design integration patterns that connect CRM, support and operational systems. Architect data flows that enable automation and AI-powered decisions. Evaluate new models and tools, and make build-vs-buy recommendations grounded in the team's real maintenance capacity. Opportunity identification (10%) Spot automation and integration opportunities others miss by understanding how work actually gets done. Translate technical possibilities into language department stakeholders can evaluate. Estimate effort honestly and scope tightly: cut work that does not change the outcome, and say so early. What You Have 5 to 8 years in software engineering, integration development or automation engineering. Production experience with a workflow platform such as n8n, Zapier or Tray.io, building automations that other teams rely on. Experience designing reliable autonomous agents: agent frameworks and patter