Sr Director, AI Transformation

This leader turns our marketing AI strategy into working capability. The strategy and priorities are set; this role makes them real in production, at the quality bar a company serving legal, tax, and risk professionals demands.

You own AI skills development: the design, build, versioning, and governance of the library of AI skills and agents that power marketing workflows end to end. You lead a team of AI engineers and are a hands-on builder yourself, not solely a manager. Delivering the strategy also depends on Teams you do not own, including engineering and data science functions; you will partner with and coordinate those Teams as dependencies, setting clear interfaces and holding delivery on track across them.

Success is not a longer list of tools or adoption dashboards. It is complete marketer workflows that work, from brief through content through channel, a foundation that is compliant and portable, and clear ownership of quality so AI improves outcomes without introducing regressions. Our internal AI credibility is a direct extension of our external brand, and this role protects it.

About the role

• Own AI skills development: architect, build, version, and maintain the skills and agents that power marketing workflows, and set the standards others build to.
• Lead and grow a team of AI engineers, and stay hands-on enough to build alongside them and set the technical bar.
• Convert the prioritized AI use-case portfolio into shipped, measurable capability against committed timelines and quality standards.
• Manage cross-functional dependencies: partner with and coordinate the engineering and data science Teams the strategy relies on, defining clear interfaces, sequencing, and accountability so delivery does not stall between teams.
• Deliver end-to-end workflows rather than fragments, enabling whole tasks from brief to finished, channel-ready output.
• Build the compliance foundation first: legal, privacy, accessibility, and content-integrity checks as reusable building blocks that everything else sits on top of.
• Establish engineering governance: a single accountable owner per skill, versioning and review discipline, monitoring for quality regressions, cost control, and guardrails for higher-risk autonomous use cases.
• Engineer for portability and avoid platform lock-in, so investment is not stranded by future platform decisions.
• Instrument outcomes that matter, including pipeline contribution, hours redeployed, time-to-insight, adoption and quality, and raise the standard for accountability and workflow redesign, not just speed.
• Partner with enterprise engineering, IT, and commercial and revenue operations so marketing AI integrates cleanly with the wider technology and commercial architecture.

About you

Required

• Hands-on experience architecting and shipping production AI applications: agentic workflows, skill and context engineering, orchestration, retrieval, and tool and API integration. You have built these systems, not just overseen them.
• A strong technical foundation in data engineering, analytics engineering, business intelligence, or software engineering: data pipelines, modern data platforms and warehouses, and the discipline to build for scale, quality, and governance. Deep data engineering experience counts fully here.
• A track record of building a capability or platform from scratch and scaling it to real, sustained usage.
• Experience leading engineers as a hands-on builder-leader, setting technical direction and raising the bar of those around you.
• Proven ability to deliver through influence and partnership across Teams you do not own, managing dependencies and holding cross-functional delivery on track.
• Sound judgment on quality, risk, and governance in production AI, and the instinct to say no to work that will not clear a value or quality bar.
• Bachelor’s degree or equivalent practical experience. We care more about what you have built than where you studied.
• Experience in marketing, growth, or commercial and Go-to-market contexts, or building tooling adopted by those functions.

Preferred

• Familiarity with common marketing, data, and commercial technology stacks, including marketing automation, CRM, web and content platforms, and BI and data-transformation tooling.
• Data science experience such as propensity, attribution, or segmentation modeling.
• Experience in a regulated or high-accuracy industry, such as legal, tax, financial services, or…