Risk Solutions Engineer

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. About the Role: Key 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. Apply techniques such as Natural Language Processing (NLP), machine learning, and LLMs to translate regulatory content, operational data, and external signals into actionable risk insights with a focus on identifying operational risk exposures, control gaps, and early warning indicators aligned to enterprise risk taxonomy. 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: We are looking for a hands-on builder and problem-solver, with typically 6 – 10 year of work experience across software engineering, data analytics, AI/ML, and/or risk & compliance domains; who combines strong engineering expertise with a deep understanding of risk and compliance, and a passion for using AI and data to transform how organizations manage risk or demonstrates a strong willingness and ability to build foundational expertise in operational risk management frameworks and practices.  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 logic into data models, automated monitoring solutions, and system-driven controls. Experience or working knowledge of operational risk management practices (e.g., risk assessments, control testing, issue management, KRIs) or demonstrated ability to rapidly upskill in these areas. Analytical & Problem-Solving Skills Ability to break down complex, ambiguous problems into clear, structured technical and analytical solutions. Strong quantitative and analytical mindset with experience deriving actionable insights from data and link analytical outputs to risk scenarios, control effectiveness, and operational risk outcomes. Stakeholder Engagement & Communication Excellent verbal and written communication skills with the ability to explain complex technical concepts to non-technical audiences. Strong data storytelling capability to influence senior stakeholders and support decision-making particularly withi