Lead AI Engineer

Job Description At Thomson Reuters, we’re transforming Service Management from a reactive, process-bound group into an AI-native, intelligence-driven organization. Operational data flows into intelligence, intelligence into orchestration, and orchestration into execution, in a closed loop with AI embedded in detection, decision, and action. Our AI Operations and Automation (AIOps) function is the intelligence layer of that model.

Intelligence only matters if people can see it, question it, and act on it. As more of our operations run through AI agents, the layer where people and agents meet the data decides whether any of it gets trusted. You’ll build that layer.

Key Responsibilities This role sits where three disciplines meet: dynamic data visualization, applied AI, and UI/UX. You’ll define how our AI agents exchange information with one another and hand it to the engineers who depend on it. You’ll design and build interactive visualizations that make dense operational telemetry immediately readable, showing what’s happening, why, what the system has already done about it, and what still needs a person.

And you’ll own the experience end to end, from first prototype to the tools our teams run on every day.

This is an internal role and an exciting one that never existed before. Your users are our own people: the Global Command Center, site reliability and platform engineers, service managers, and the leaders who need to read the health of our systems at a glance. Their trust in what they see is the product.

We don’t have a template for this role, and that’s the point. You’ll help define what it becomes.

You’ll join Service Management and Transformation, part of the CIO segment of our Technology organization, and work alongside AIOps, the team building the models and automation behind the interfaces you design. We keep a clear separation between building and running, so you’ll have room to build. The role is hybrid, based in our Dallas, TX office.
· Build dynamic data visualization. Design and engineer interactive, real-time views of operational data, including topology and dependency graphs, event and alert streams, incident timelines, and predictive signal. Think D3, WebGL, and modern front end, not drag-and-drop BI.
· Define the agent-to-agent interface. Establish the interfaces and data contracts that let AI agents pass structured, verifiable information to one another, and hand it to engineers in a form they can inspect and challenge.
· Make automated action visible. Our L0 tier is where AI and automation resolve issues ahead of human responders. Show what it did, why, and how confident it was, so engineers supervise machine decisions rather than simply receive them.
· Design the experience. Lead the UI/UX and information architecture: research how responders work under pressure, decide what they need to see and when, shape the interaction model, and make a complex system feel simple to someone working an incident at 3 am.
· Prototype and ship. Turn ambiguous problems into working software quickly, then harden the strongest ideas into tools our teams depend on.
· Set technical direction. As a lead, you’ll set the standard for the experience layer, make the architectural calls, and mentor engineers and designers. A builder first, a coach alongside.
· Partner closely. Work day to day with AIOps, Service Data and Insights, Configuration and Topology, Workflow Engineering, and the Global Command  your first year, you might:
· Replace an operational report our teams read line by line with an interactive view that answers the question in seconds.
· Publish the first version of our agent-to-agent data contract, and prove it with two agents working together in production.
· Ship the interface that…