Senior Engineer, Risk Analytics

As a Risk Analytics Engineer, you are the critical bridge between advanced analytics and our production environment. You will be embedded within a cross‑functional squad, responsible for the operationalization of risk models and strategies. Your primary mission is to ensure that the analytical solutions built by our data scientists and risk analysts—from credit scorecards to real‑time fraud models—are deployed, monitored, and managed in a robust, automated, and scalable fashion.

You will build and own the Machine Learning Operations pipelines and solutions that bring our risk intelligence to life.

• Design, build and maintain automated Continuous Integration and Continuous Delivery/Deployment pipelines to test, validate and deploy risk models and decisioning logic.
• Package (containerize) and deploy Machine Learning models and analytical engines as secure, versioned, and low‑latency APIs, creating a “Risk‑as‑a‑Service” capability.
• Implement and manage comprehensive monitoring solutions for deployed models, tracking data drift, model performance degradation, and technical health (latency, errors).
• Work with Decisioning Configuration Analysts to automate the deployment and testing of business rules and strategies.
• Collaborate with Data Scientists to refactor and optimise their code for production; work with Data Engineers and Platform Engineers to ensure seamless integration and performance.
• Champion software engineering best practices within the risk analytics team and contribute to the evolution of our Machine Learning Operations competency.

• Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related quantitative field.
• 3–5+ years experience in a relevant technical role such as Dev Ops Engineer, Machine Learning Operations Engineer, Software Engineer, or Data Engineer with a focus on automation.
• Strong programming proficiency, particularly in Python.
• Proven experience with Continuous Integration and Continuous Delivery/Deployment tools (e.g., Git Hub Actions, Azure Dev Ops, Jenkins).
• Hands‑on experience with cloud platforms (AWS or Azure).
• Experience with containerisation technologies and distributed computing (Docker, Kubernetes).
• Familiarity with Infrastructure as Code tools (Terraform, Cloud Formation).

• Adopting practical approaches
• Articulating information
• Communication and collaboration skills
• Problem solving
• Attention to detail
• Managing tasks
• Output driven

• Strong capability in modern data and Machine Learning operations, including orchestrating workflows, managing model life cycles, and handling large‑scale data processing.
• Solid understanding of risk analytics within financial‑services or other regulated environments.
• Ability to integrate and ope rationalise models developed across diverse analytical and statistical toolsets.
• Practical experience implementing advanced AI solutions, including large‑scale language models, retrieval‑based architectures, and vector‑driven search.