Director of AIOps

Director of AIOps

Ready to build the intelligence layer that makes global operations predictive, autonomous, and self-healing at Thomson Reuters? 

At Thomson Reuters, we build technology that helps professionals pursue justice, truth, and transparency. We are transforming Service Management from a reactive, process-bound group into a proactive, intelligence-driven organization — an AI-native, autonomy-ready function that delivers predictive insights and automated resolutions with humans providing strategic oversight. Within our new operating model, data flows into intelligence, intelligence into orchestration, and orchestration into execution, forming a closed-Loop system in which AI is embedded in the detection, decision, and action loops. The AI Operations and Automation function is the intelligence layer — the "brain" — of that model. 

We are seeking a Director of AIOps to lead this function, responsible for developing and deploying the AI/ML capabilities and automation logic that drive predictive, autonomic IT operations. This is one of our most important new hires: a hands-on "player-coach" who will lead a small team of ML, automation, and AIOps engineers while remaining deeply engaged in the technology. You will own the capabilities that detect issues before they impact customers, correlate signal from noise, and resolve incidents automatically. A central mandate is to define, drive, and implement the concept of "L0" — a new autonomous tier in which AI and automation act ahead of the Global Command Center (GCC), reducing MTTR and preventing incidents before customer impact, with human responders operating as "L1" for exceptions and humans on the Loop for oversight. 

About the Role 

In this opportunity as Director of AIOps, you will: 

• Lead the newly-formed AIOps (AI Operations and Automation) function — the intelligence layer, or "brain," of our Service Management operating model — building and deploying the AI/ML models and automation logic that drive predictive, autonomic operations. 

• Recruit, build, and lead a high-performing team of AIOps architects, ML engineers, and automation engineers, remaining hands-on as a player-coach who sets technical direction by example. 

• Select, architect, and own the AIOps platform and telemetry pipeline — spanning ServiceNow Event Management, correlation and enrichment, and custom or third-party AIOps tooling — and mature the observability practice across metrics, logs, traces, and events, instrumenting for machines rather than dashboards alone. 

• Design event correlation and noise-reduction logic that collapses redundant alerts into single, actionable incidents (targeting a 50%+ reduction in alert noise), and build anomaly-detection and predictive incident models that surface silent failures before customer impact. 

• Develop automated remediation logic, multi-step automations, and self-healing runbooks that resolve issues without human intervention (driving a 40–60% MTTR reduction for automation-eligible incidents), engineered with the rigor of production software as a centralized, reusable library with testing, version control, and rollback readiness. 

• Drive and implement the concept of "L0" across Service Management — establishing a new autonomous tier in which AI-driven proactive resolutions operate as L0, GCC engineers operate as L1 for exceptions, and L2 is reserved for genuine major incidents — and build AI-driven change risk-scoring models and LLM-based operational assistants that strengthen governance and accelerate root-cause analysis, keeping humans on the loop for oversight. 

• Own the AIOps roadmap and shared operational OKRs — percentage of incidents auto-resolved, MTTR reduction, and prediction precision/recall — while partnering across the Service Management operating model (Service Data and Insights, Configuration and Topology, Workflow Engineering, and the GCC) under a clear "build vs. run" separation of concerns. 

About You 

You're a fit for the role of Director of AIOps if your background includes proven, hands-on AI enablement in operations — you have deployed and operationalized AI/ML in a live operations or service management environment and can demonstrate the measurable outcomes it delivered. Specifically: 

• A minimum of 10 years of industry experience in technology operations, DevOps, SRE, or platform engineering, including at least 2 years in a hands-on AIOps leadership role; bachelor's degree in a technical field (Computer Science, Engineering, or Data Science) required, with a master's and relevant certifications (cloud, ML, SRE, ITIL, or ServiceNow) preferred. 

• Deep, hands-on experience applying AI/ML to IT operations — event correlation, anomaly detection, predictive incident modeling, and noise reduction at enterprise scale — grounded in the data science of operations (time-series analysis, classification, precision/recall, and retraining on operational feedback). 

• Fluency with AIOps platforms (e.g., ServiceNow Event Management, or comparable commercial or open-source AIOps tooling) and the pipelines that ingest and enrich operational telemetry. 

• Proven experience designing and scaling automated remediation, orchestration, and self-healing runbooks in production — treated as software engineering (reusable libraries, testing, version control, measurable toil reduction) and executed within well-designed safety guardrails such as approvals, rollback, and built-in observability. 

• Experience establishing autonomous operating tiers — such as an AI and automation "L0" ahead of human responders — including the decision gates, readiness criteria, and guardrails that make them safe to run in production. 

• Strong background in observability and event management, with a track record of defining and driving operational outcomes (MTTR, MTTD, auto-resolution rate, availability/SLOs) and hands-on fluency with modern AI and agentic tooling for operations (LLM-assisted RCA, agentic SRE tools) — and an honest understanding of their failure modes. 

• Demonstrated ability to lead and grow small, high-caliber engineering and AI operations teams (AI/ML, automation, SRE) while remaining personally hands-on — with the technical depth to earn credibility and the organizational clout and stakeholder skills to drive change across operations, data, governance, and engineering, maintaining a clear build-vs-run separation with the Global Command Center. 

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