GBG

Underwrites and manages global insurance policies

Machine Learning Engineer

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeKuala Lumpur, Federal Territory of Kuala Lumpur, MalaysiaNo salary listedHybrid

About the Job

Requirements

At least 3 years of experience building and deploying production machine learning systems in Python.
Working knowledge of cloud-native machine learning platforms, including AWS SageMaker, Azure ML, or GCP Vertex AI, and containerization tools such as Docker and Kubernetes.
Hands-on experience with continuous integration and continuous delivery for machine learning pipelines.
Experience with fraud detection and anti-money laundering models.
Must be eligible to work in Malaysia.

Responsibilities

Design, develop, and deploy machine learning models for fraud and anti-money laundering detection for batch and real-time transaction scoring.
Build and maintain MLOps pipelines for model training, validation, deployment, monitoring, and retraining using tools such as MLflow, Tecton, or equivalent feature stores.
Collaborate with data engineers to design feature engineering pipelines and maintain the Predator feature dictionary and synchronization mechanisms.
Optimize model performance to meet latency and transactions-per-second targets 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 to align machine learning platform choices with the overall modernization architecture.
Monitor advances in fraud detection machine learning, 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.

Machine Learning Engineer (4023)

GBG · Kuala Lumpur, Malaysia (Hybrid) · Full-time · Posted 2026-09-25

Workplace: hybrid

Department: Technology and Operations

Description

Enabling safe and rewarding digital lives for genuine people, everywhere

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.

Requirements

Skills 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.

Apply

Apply at GBG

About the Company

GBG

Underwrites and manages global insurance policies

GBG Insurance Limited provides a range of global insurance products, including healthcare, disability, travel, and life insurance, mainly by underwriting policies and collecting premiums from individual and corporate clients. The company funds its operations by investing the collected premiums to pursue financial stability and profitability. It differentiates itself through tailored insurance solutions across a diverse client base and by operating under strict regulatory standards to protect policyholders. GBG’s current administration, with appointed joint administrators, focuses on managing the company’s affairs while ensuring that existing claims and medical treatments are honored and customers remain served. The company’s goal is to maintain reliable insurance services, satisfy customers, and preserve financial stability during the administration process.

Company size: 201-500
Company stage: Late Stage VC
Total funding: $16M
Headquarters: West Windsor Township, NJ
Founded: 1981

Recently Posted Jobs

Explore more opportunities at GBG

See more jobs