Support at scale is a product problem, and Mews is hiring the person who will own it end to end. As Director of Product & Engineering, Support, you will build the AI-powered systems that determine how tens of thousands of hospitality operators get help, find answers, and never have a reason to need either. This is not a platform stewardship role. It is a proprietary build problem in a market where no one has solved it well yet.

You will report to the Senior Director of Growth and hold full accountability for the strategy, execution, and culture of the Support tribe. Your scope covers the full AI-powered support and self-service layer (currently built alongside Ada, with internal agents already in development), the product knowledge infrastructure that keeps humans and AI systems accurate as Mews ships fast, and the feedback loop that turns contact volume into engineering prioritisation. This tribe is one of the most influential inputs to the Mews product roadmap. That influence is yours to build and defend.

The boundary is deliberate: your tribe owns what the product does and whether the customer understands it. Everything beyond that (helping customers maximise value from what they have) sits with PLG & CS. Within your boundary, you make the build vs. buy calls, own the architecture, and ship.

What you would do 

• Own the full AI-powered support and self-service platform: decide whether Mews extends Ada, migrates to a different platform, or doubles down on the internal agents already in development. Own the configuration, the systems layer, and the platform architecture regardless of which direction you take it. Treat contact rate, chatbot containment and quality, FCR, AHT, and wait time as product outcomes the platform is accountable for moving.
• Design, build, and ship AI-powered resolution and escalation systems: define where LLM-backed retrieval and conversational AI add genuine value in the support motion, where they create friction, and how to measure the difference. Own the evaluation frameworks and the architectural decisions that determine whether Mews builds a proprietary edge or configures toward commodity.
• Own product knowledge infrastructure: the canonical source of truth for how the product works, including help content, release notes, changelogs, and agent-facing knowledge. Build the systems that keep it accurate and discoverable as the product evolves.
• Build and operate the product feedback loop: cluster support tickets by root cause, weight each pattern by ARR exposure and resolution cost, and deliver that intelligence to R&D in a format that moves prioritisation decisions. Track whether the issues you surface get fixed. Measure the support volume reduction when they do.
• Track the support impact of every product launch and release, identify which releases generate disproportionate contact volume, and feed that analysis back to R&D and Product Marketing before the next launch cycle.

AI Fluency Level 4: In this role, that means you have built AI-powered products or systems where the model is a core architectural component, not a feature layer. You understand LLM and RAG integration patterns well enough to make engineering decisions about them, you have shipped conversational AI or knowledge retrieval systems at scale, and you can evaluate what you have built rather than accepting output at face value. At Mews, you will own the architecture of our AI support stack and the build direction that decides whether it becomes a genuine competitive advantage.

For more information on AI fluency at Mews, please refer to "AI Fluency at Mews: A Comprehensive Guide for Candidates" on Confluence.

What you would bring 

• 10+ years in product and/or engineering roles, with at least 5 years at Director level or equivalent, leading cross-functional tribes in a B2B SaaS company at meaningful scale.
• Demonstrated experience owning AI-powered products or systems: LLM/RAG integration patterns, conversational AI evaluation frameworks, and platform architecture decisions, with shipped outcomes to point to.
• A strong point of view on AI in customer support: where it adds genuine value, where it creates friction, and how you measure the difference. Experience with Ada, Intercom, Zendesk, or similar platforms is a strong plus.
• A track record of making build vs. buy calls with real commercial and product rigour, and living with the consequences of those decisions.
• Experience leading Product Builders or similarly autonomous product-building roles, setting strategic context that enables fast, independent execution rather than managing through detailed specs.
• AI Fluency Level 4, or the hands-on engineering equivalent: you have built AI-powered systems, not just configured them.

Nice to have

• Familiarity with hospitality or other operationally intensive SaaS verticals.
• Experience with CI/CD, automated testing, and operational resilience as built-in delivery culture rather than bolted-on process.
• Prior experience working at the intersection of support operations and product engineering, where you have translated contact volume into roadmap prioritisation.