Lead, CS AI Content

Flex•Salt Lake, NY

1d•Hybrid

About The Position

We're hiring a Lead, CS AI Content to own the content layer that powers Flex's AI-driven customer support tools. This role sits at the intersection of content, conversational AI, and support operations. You'll build and maintain structured, machine-readable content that enables AI systems to respond accurately, take action through APIs, and safely support customers across channels. You'll play a key role in conversational AI by authoring dialogue flows, maintaining decision trees, and ensuring safe, accurate customer experiences. The content you produce directly impacts containment, CSAT, and risk prevention.

Requirements

Experience in Customer Success or Support, Content Operations, Trust & Safety, QA, or similar fields.
Hands-on experience with conversational AI or AI-powered support tools.
Experience working with systems and workflows that interface with automation or AI tools.
Strong attention to detail and ability to execute with structure in ambiguous environments.
Ability to collaborate effectively with cross-functional partners including Product, Engineering, and Ops.

Nice To Haves

Experience in high-growth startups, fintech, payments, or customer support operations preferred.
Experience with chatbot authoring, AI evaluation, or support QA.

Responsibilities

Write and maintain AI-native content across chatbot, voicebot, and agent assist tools, including structured responses, decision trees, and escalation logic.
Author conversational AI dialogue flows, including multi-turn interactions, clarifying questions, and safe handoffs to human support.
Improve AI workflows by working with API-connected tools, helping ensure AI can retrieve data, trigger actions, or route conversations appropriately.
Keep content and configurations current across all AI tools — ensuring chatbot, voicebot, and agent assist are consistently optimized and aligned as products, policies, and workflows evolve.
Identify gaps in SOPs and support documentation that limit AI accuracy and flag them for resolution.
Update SOPs and internal documentation to reflect product changes, policy updates, and evolving AI workflows — keeping content accurate and agent-ready.
Disambiguate overlapping or ambiguous concepts to improve AI reasoning and response quality.
Drive content for cross-functional launches as new AI tools or capabilities roll out.
Maintain version-controlled AI training content that is audit-ready and aligned with policy and regulatory requirements.
Monitor chatbot and voicebot analytics to identify underperforming flows, containment gaps, and content issues — and surface or address them based on priority.