Define and implement the Content Ops model, including governance, voice & tone frameworks, and content standards
Design and scale omnichannel conversational systems (chatbots, voice, messaging, email, in-product experiences)
Build and manage modular, reusable conversational flows aligned with user journeys and business goals
Establish semantic QA frameworks to ensure consistency, accuracy, and brand alignment across channels
Design and optimize end-to-end content workflows, integrating Product, Marketing, CX, and Engineering
Define and manage tooling architecture, including conversational platforms and content automation tools
Leverage AI/NLP and LLMs to design, generate, and optimize conversational experiences
Train, fine-tune, and evaluate language models and conversational systems
Integrate platforms such as Voiceflow, Dialogflow, Rasa, ChatGPT, Braze, Twilio, Sendgrid
Define and track OKRs and KPIs, including comprehension rates, resolution rates, engagement, and conversion
Drive content personalization and automation strategies at scale
Act as a strategic bridge between Product, Marketing, Data, and Engineering teams
Influence stakeholders and scale adoption of Content Ops practices across the organization
Proven experience in Content Ops, Conversational Design, or CX/UX Writing at scale
Strong experience designing conversational experiences across multiple channels
Hands-on experience with NLP, LLMs, or AI-driven content systems
Experience with conversational tools such as Voiceflow, Dialogflow, Rasa, or similar
Familiarity with CRM/engagement platforms (Braze, Twilio, Sendgrid)
Strong understanding of content lifecycle, governance, and modular content systems
Experience defining voice & tone systems and content guidelines
Use Generative AI coding tools (e.g., GitHub Copilot, Cursor) as a first-class engineering assistant for:
Code scaffolding and refactoring
Code generation and optimisation
Test-cases and documentation generation
Build applications through AI-driven development practices, including:
AI-assisted debugging and troubleshooting
Intelligent code completion and pattern recognition
Automated documentation generation
Apply prompt engineering best practices for reliable, repeatable engineering outcomes.
Validate GenAI output (determinism checks, guardrails, fallback logic).
For more on our team culture and benefits, check out our careers page.
