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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr Director, AI Transformation - **Company:** Thomson Reuters Holdings Inc. - **Location:** Eagan, MN, United States - **Experience:** Expert - **Salary:** $157,500.0 - $292,500.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Information Engineering, Data Infrastructure, Automation of Marketing, Software Engineering, Management of Software Versions, Prompt Engineering, Agentic-AI, Functional Dependencies, Data Pipelines, Service Stack - **Published:** October 5, 2026 - **Apply:** https://thomsonreuters.wd5.myworkdayjobs.com/External_Career_Site/job/United-States-of-America-Eagan-Minnesota/Sr-Director--AI-Transformation_JREQ202835-1 ## About the Role 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 healthcare, where compliance and correctness are non-negotiable. * A record of shipping AI or automation capability that a business function adopted at scale. ## Description 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.