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: 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. 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. 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. 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. 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: 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. 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. 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. 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. 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 within risk governance forums (e.g., risk committees, audit discussions).   Demonstrated ability to take ownership and drive initiatives end-to-end, from concept and design through to implementation and continuous improvement. Experience working cross-functionally and operating effectively in a fast-paced, evolving environment. Curious, forward-thinking, and comfortable experimenting with emerging technologies and new approaches. High degree of adaptability with the ability to pivot based on evolving business priorities and technological advancements. Demonstrated ability and willingness to build expertise in operational risk frameworks, risk taxonomy, and enterprise risk management processes where prior experience is limited #LI-AD2 What’s in it For You? Hybrid Work Model: We’ve adopted a flexible hybrid working environment (2-3 days a week in the office depending on the role) for our office-based roles while del