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 analystsfrom credit scorecards to real-time fraud modelsare 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.

Machine Learning Operations Pipeline Development (Continuous Integration and Continuous Delivery/Deployment): Design build and maintain automated Continuous Integration and Continuous Delivery/Deployment pipelines to test validate and deploy risk models and decisioning logic.

Model Deployment & Serving: Package (containerize) and deploy Machine Learning models and analytical engines as secure versioned and low-latency APIs creating our Risk-as-a-Service capability.

Production Monitoring: Implement and manage comprehensive monitoring solutions for deployed models tracking data drift model performance degradation and technical health (latency errors).

Automation of Strategy: Work with Decisioning Configuration Analysts to automate the deployment and testing of business rules and strategies.

Collaboration & Enablement: Work side-by-side with Data Scientists to refactor and optimize their code for production. Collaborate with Data Engineers and Platform Engineers to ensure seamless integration and performance.

Tooling & Best Practices: Champion software engineering best practices within the risk analytics team. Contribute to the evolution of our Machine Learning Operations competency.

Qualifications :

Qualification: 

• Bachelors degree in Computer Science Software Engineering Information Systems or a related quantitative field.

Experience Required

• 3-5 years experience in the relevant technical role such as DevOps Engineer Machine Learning Operations Engineer Software Engineer or Data Engineer with focus on automation.
• Strong programming proficiency particularly in Python.
• Proven experience with Continuous Integration and Continuous Delivery/Deployment tools (e.g. Github actions Azure DevOps 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)

Additional Information :

Behavioural Competencies:

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

Technical Competencies:

• Strong capability in modern data and Machine Learning operations including orchestrating workflows managing model lifecycles and handling largescale data processing.
• Solid understanding of risk analytics within financialservices or other regulated environments.
• Ability to integrate and operationalize models developed across diverse analytical and statistical toolsets.
• Practical experience implementing advanced AI solutions including largescale language models retrievalbased architectures and vectordriven search.

Remote Work :

No

Employment Type :

Full-time