We make it our mission to ensure more genuine people have digital access to opportunities, and businesses have access to more genuine people. Our technology draws on diverse and reliable data to create a single point of truth for identity and address verification.
With over 30 years of experience behind us our team and technology are focused on enabling safe and rewarding digital lives for everyone. Regardless of age, location or background, genuine people everywhere should be able to digitally prove who they are and where they live.
About the team and role
Global Fraud Solutions
The team provides decision support solutions to address business objectives in risk prevention and fraud detection. We deliver software solutions and offer client support using our expertise and a client-focused approach.
Machine Learning Engineer
Working closely with Software Engineering, Product and Data Scientist teams, the Machine Learning Engineer will advance ML capabilities within the GFS fraud detection platforms. You will work on the delivery of the ML roadmap and building robust MLOps pipelines. You will apply your expertise in machine learning to real-world fraud detection challenges faced by banking and fintech customers globally.
What you will do- Design, develop, and deploy machine learning models for fraud and AML detection, supporting both batch and real-time transaction scoring scenarios.
- Build and maintain MLOps pipelines covering model training, validation, deployment, monitoring, and retraining workflows using modern tooling (e.g. MLflow, Tecton, or equivalent feature stores).
- Collaborate with data engineers to design feature engineering pipelines and maintain the Predator feature dictionary and sync mechanisms.
- Optimise model performance to meet strict latency and TPS targets required for real-time fraud decisioning.
- Conduct model validation, A/B testing, permutation importance analysis, and champion/challenger evaluations to ensure model quality.
- Work with the Architecture Review Committee (ARC) to align ML platform choices with the overall modernization architecture.
- Stay current with advances in fraud detection ML — including graph-based models, anomaly detection, and generative AI applications — and propose relevant adoptions.
- Mentor junior team members and contribute to knowledge sharing across squads.
RequirementsSkills we’re looking for
- 3+ years building and deploying production ML systems in Python.
- Working knowledge of cloud-native ML platforms (AWS SageMaker, Azure ML, GCP Vertex AI) and containerisation (Docker, Kubernetes).
- Hands-on experience with CI/CD for ML pipelines.
- Experience with fraud detection and AML models.
- Eligible to work in Malaysia.
Benefits
Skills Required
- 3+ years building and deploying production ML systems in Python.
- Working knowledge of cloud-native ML platforms (AWS SageMaker, Azure ML, GCP Vertex AI).
- Containerisation experience (Docker, Kubernetes).
- Hands-on experience with CI/CD for ML pipelines.
- Experience with fraud detection and AML models.
- Eligible to work in Malaysia.
What We Do
Global digital identity and fraud solutions, working to create a world where everyone can transact online with confidence Our market-leading technology, data and expertise help our customers improve digital access, deliver a seamless experience and establish trust so that they can transact quickly, safely and securely with their customers online. Headquartered in the UK and with over 1,000 team members across 16 countries, we work with 20,000 customers in over 70 countries. Some of the world's best-known businesses rely on GBG to provide digital services and keep the economy moving, from US e-commerce giants to Asia's biggest banks and European household brands.
