Risk Solutions Engineer

Responsibilities

• Build AI-Powered Risk Solutions
• Design, develop, and deploy intelligent workflows, automation solutions, and AI-driven agents to detect, analyse, and monitor risks across structured and unstructured data.
• Engineer Scalable Risk Intelligence Platforms
• Build and maintain data pipelines, models, and architectures that enable near real-time risk monitoring and insight generation aligned to enterprise risk needs.
• Develop reusable, modular components and platforms that scale across business units and support enterprise-wide risk visibility.
• Translate Risk Frameworks into Automated Controls
• Convert risk taxonomy, control requirements, and issue management frameworks into machine-readable rules, automated controls, and monitoring mechanisms.
• Design and embed preventive and detective controls within systems and workflows to strengthen risk mitigation effectiveness.
• Partner with Risk & Compliance teams to ensure alignment with established operational risk methodologies, including risk assessments, control evaluations, and issue remediation frameworks.
• Drive Data-Driven Risk Decisioning
• Develop predictive analytics and modelling capabilities to identify emerging risks, anomalies, trends and support ongoing monitoring of key risk indicators (KRIs), control effectiveness, and risk appetite thresholds.
• Deliver decision-ready insights through BI dashboards and AI-driven tools, enabling leadership forums to focus on critical risk signals and deviations.
• Embed Responsible AI and Governance
• Ensure all solutions adhere to Responsible AI principles, including transparency, explainability, bias mitigation, and alignment to internal and regulatory standards.
• Integrate outputs into Enterprise Risk Management (ERM) processes, including risk assessment, issue lifecycle management, and reporting while ensuring traceability of risk decisions and alignment with audit and assurance expectations.
• Partner and Influence Across Stakeholders
• Serve as a technical liaison between Risk, Compliance, Technology, and Data teams, translating complex analytical outputs into actionable business insights.
• Use data storytelling to influence decision-making and drive adoption of technology-enabled risk management practices and supporting the embedding of a consistent risk-aware culture across business and technology teams.
• About You:
• Required Skills & Experience
• Software Engineering and Data Expertise
• Strong hands-on experience in programming (e.g., Python, Java) with a proven track record of building and deploying scalable applications, automation solutions, or data pipelines.
• Experience in designing data architectures, integrating multiple data sources, and ensuring data quality and reliability.
• AI / Machine Learning Capabilities
• Demonstrable experience or strong working knowledge of AI/ML techniques, including NLP, LLMs, and anomaly detection.
• Experience applying AI to real-world problems such as unstructured data analysis, predictive modelling, or intelligent automation.
• Risk & Compliance Acumen
• Strong understanding of enterprise risk management concepts, including risk identification, assessment, controls, KRIs, and issue lifecycle management.
• Ability to translate risk frameworks and control

Additional Information

As a content-driven technology company, Thomson Reuters operates in a uniquely complex and regulated environment and is evolving toward a real-time, data-driven risk and compliance model, moving beyond periodic, retrospective processes toward intelligent, automated systems. This role is a foundational capability builder within the Risk & Compliance function, responsible for architecting and delivering AI-powered, scalable solutions that transform how risk is identified, monitored, and managed across the enterprise. You will operate at the intersection of technology, data, and risk management, partnering closely with Risk, Technology, and Data teams to operationalize a proactive, forward-looking risk capability while embedding core operational risk management principles, including risk identification, assessment, control design, and issue lifecycle management within technology-driven solutions.

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