Lead AIOps & Automation Architect

Thomson Reuters is transforming Service Management into an AI‑native, autonomy‑ready operating model. The Lead AIOps & Automation Architect plays a critical role in this transformation by designing the intelligence layer that enables predictive insights, intelligent decisioning, and automated actions across the full service management lifecycle. This role is deeply hands‑on and outcome‑driven. You will define how telemetry, service topology, and service management data are converted into AI‑driven decisions and automated actions—integrated directly into production workflows, release and change processes, and real‑time operational execution. The role is an individual contributor with significant architectural ownership and cross‑functional influence, focused on building production‑grade, governed, and scalable AI‑driven operations. About the Role In this opportunity as Lead AIOps & Automation Architect, you will: Key Responsibilities AIOps Architecture & Strategy Define and own the end‑to‑end AIOps and automation architecture, including data ingestion, feature engineering, model selection, inference, and automation triggers. Design scalable architectures for event correlation, anomaly detection, predictive incident modeling, noise reduction, and operational decision support. Establish architectural patterns that support human‑on‑the‑loop and human‑above‑the‑loop operational models, including clear autonomy levels and guardrails. AI‑Driven Automation & AI‑Native Service Management Architect automation frameworks that support intelligent incident response, change execution, release validation, and post‑deployment decisioning. Define AI‑native service management patterns for routing, enrichment, summarization, prioritization, and risk‑aware decision support across incident, change, release, and request workflows. Partner with workflow and platform engineering teams to ensure AI outputs are embedded into production‑grade workflows, not standalone tools. Ensure automations are safe, observable, auditable, reversible, and compliant with governance and access controls. Data, Telemetry & Operational Context Work closely with service data and insights teams to define AI‑ready datasets, telemetry standards, and data quality requirements. Leverage metrics, logs, events, traces, service topology, and service management data as model inputs. Design cost‑ and signal‑aware telemetry ingestion and correlation strategies appropriate for enterprise scale. Ensure alignment with observability standards, monitoring‑as‑code initiatives, and topology‑driven context. Operational Intelligence & Model Lifecycle Design and evaluate ML approaches for operational and service‑management use cases such as time‑series forecasting, clustering, classification, root‑cause support, change risk scoring, and release health assessment. Define success metrics for models, including precision, recall, MTTR impact, delivery velocity impact, and false‑positive rates. Establish feedback loops that continuously improve models and automation based on real operational outcomes and execution data. Technical Leadership & Influence Serve as a technical authority for AIOps and automation across Service Management. Collaborate with SREs, platform engineering, incident leaders, release engineers, and data teams. Influence standards, tooling decisions, and architectural direction without direct people management. Provide architectural guidance, patterns, and technical direction to senior and lead engineers. About You You’re a fit for the role of Lead AIOps & Automation Architect if you have the following required qualifications: Background & Experience Required Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related technical field, or equivalent practical experience. 10 years of experience in IT operations, reliability engineering, platform engineering, observability, or automation‑heavy enterprise envir ... (truncated, view full listing at source)