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 environments. Proven experience designing or implementing AIOps, event management, intelligent automation, or AI‑assisted service management solutions. Strong background in operational telemetry, monitoring, and large‑scale distributed systems. Hands‑on experience integrating AI/ML outputs into real operational and service‑management workflows (not analytics‑only solutions). Preferred Experience with ServiceNow ITOM, Event Management, AIOps, or comparable enterprise ITSM platforms. Exposure to time‑series analysis, anomaly detection, or ML‑driven operational decisioning. Experience working with cloud and hybrid environments at enterprise scale. Prior background in SRE, DevOps, platform engineering, or release engineering roles. Skills & Qualifications Technical Skills Observability and telemetry platforms (metrics, logs, events, traces) Event correlation, noise reduction, and signal hygiene ServiceNow ITOM and workflow orchestration patterns Automation frameworks, runbook orchestration, and multi‑step workflow execution Safe automation design, including guardrails, escalation paths, and auditability Data pipelines and feature engineering for operational ML API‑driven integrations and workflow orchestration Service topology, dependency mapping, and impact analysis Cost‑aware telemetry ingestion and correlation strategies Professional Skills Strong systems thinking and architectural rigor Analytical and problem‑solving mindset grounded in production realities Ability to translate ambiguity into executable designs and standards Comfortable influencing senior technical and operational leaders Clear communicator across engineering, operations, and leadership audiences What Success Looks Like Measurable reduction in alert noise and incident volume. Faster detection, diagnosis, and resolution of operational issues. Improved change safety and release confidence through AI‑assisted decisioning. Increased percentage of incidents and operational