Machine Learning Engineer (4023)

GBG Malaysia; Kuala Lumpur; On-site; MYR 120,000 - 180,000; Full time

Job summary

GBG Malaysia is seeking a Machine Learning Engineer to advance ML capabilities within Global Fraud Solutions. You will collaborate with Software Engineering, Product and Data Scientist teams to deliver the ML roadmap and build robust MLOps pipelines that support real-world fraud detection challenges faced by banking and fintech customers globally.

You will apply your expertise in machine learning to real-world fraud and AML use cases, optimizing latency and accuracy for batch and real-time

Qualifications

• 3+ years building and deploying production ML systems in Python.
• Experience with cloud-native ML platforms and containerisation.
• Eligible to work in Malaysia.

Responsibilities

• Design, develop, and deploy ML 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 (ML)

Cloud platforms

Containerisation

CI/CD for ML

Fraud/AML modeling

Malaysia work eligibility

Tools

Docker

Kubernetes

AWS SageMaker

Azure ML

GCP Vertex AI

Job 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.

GBG is a global specialist in Identity Data Intelligence. We help organisations make decisions about the customers they serve and the people they employ.

Through our fundamental belief that the digital economy relies on everyone having access to data they can trust, GBG enables companies and governments to fight fraud and cybercrime, to improve the customer experience and help to protect the more vulnerable people in our society.

Headquartered in Chester (UK) and operating on 21 locations in 13 countries, GBG provides solutions to many of the world's biggest organisations, from established brands like Nike and Harrods to disruptive newcomers such as TaskRabbit and Stripe.

Find out more about how we use identity intelligently by visiting www.gbgplc.com, following us on Twitter @gbgplc and reading our blog: www.gbgplc.com/uk/blog