Fraud Detection ML Engineer — Real-Time, Scalable Pipelines

GBG Plc; Kampung Malaysia Tambahan; On-site; MYR 120,000 - 240,000; Full time

Job summary

GBG Plc is seeking a Machine Learning Engineer to advance ML capabilities in GFS fraud detection platforms. You will collaborate with Software Engineering, Product and Data Scientist teams to deliver the ML roadmap and build robust MLOps pipelines for real-time and batch scoring.

You will apply expertise in fraud detection and AML models, optimize latency, mentor juniors, and stay ahead with graph-based models and AI advancements across global banking and fintech clients.

Qualifications

• 3+ years building and deploying production ML systems in Python.
• Experience with 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.

Responsibilities

• 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.

Skills

Python

Production ML

MLOps

CI/CD for ML

Fraud/AML domain

Cloud platforms

Docker/Kubernetes

Malaysia work eligibility

Tools

Docker

Kubernetes

MLflow

Tecton

Job description

GBG Plc is seeking a Machine Learning Engineer to advance ML capabilities in GFS fraud detection platforms. You will collaborate with Software Engineering, Product and Data Scientist teams to deliver the ML roadmap and build robust MLOps pipelines for real-time and batch scoring.

You will apply expertise in fraud detection and AML models, optimize latency, mentor juniors, and stay ahead with graph-based models and AI advancements across global banking and fintech clients.