Principal MLOps Engineer

PayNet (Payments Network Malaysia); Kuala Lumpur; On-site; MYR 180,000 - 300,000; Full time

Job summary

PayNet (Payments Network Malaysia) is seeking a senior MLOps Engineer to own productionisation of fraud and risk ML platforms. You will build and operate CI/CD pipelines, govern model lifecycles, and architect secure AWS and on‑prem hybrid infrastructure.

You will partner with data scientists to deliver reliable, auditable scoring services. You will design platform standards using IaC, containerization, and IAM, ensuring regulatory compliance while maintaining stability at scale.

Qualifications

• Experience building and operating production ML systems, MLOps platforms, or large-scale DevOps environments.
• Strong proficiency in Python for ML pipelines, model packaging, automation, and service integration.
• Deep hands-on expertise with AWS architecture, including secure networking and high-availability design.
• Proven ability to design CI/CD pipelines for ML services with gated releases and controlled promotion.
• Experience with IaC tools such as Terraform and container orchestration using Kubernetes and Helm.
• Familiarity with distributed workloads (e.g. Apache Spark or Ray) and orchestration tools such as Apache Airflow or Prefect.

Responsibilities

• Own end-to-end MLOps productionisation for fraud and risk intelligence use cases.
• Build and operate CI/CD pipelines for model and service release.
• Design and enforce model lifecycle management, including versioning, retraining, and redeployment.
• Architect and operate secure AWS and on-premises hybrid infrastructure for ML platforms.
• Implement platform standards using Infrastructure as Code (IaC), containerisation, and IAM.
• Ensure deployments meet audit, security, and regulatory requirements without sacrificing stability.

Skills

Python for ML pipelines

AWS architecture

CI/CD for ML services

IaC (Terraform)

Kubernetes & Helm

Security & compliance

Model lifecycle management

Distributed workloads (Spark/Ray)

IAM / security governance

Tools

Terraform

Kubernetes

Apache Airflow

Prefect

Apache Spark

CI/CD tooling

Job description

• National payments infrastructure with real economic and systemic impact
• Organisation entering a phase of greater scale, scrutiny, and performance expectations
• People function expected to shape outcomes, not just run processes

Why PayNet / Why Now

• National payments infrastructure with real economic and systemic impact
• Organisation entering a phase of greater scale, scrutiny, and performance expectations
• People function expected to shape outcomes, not just run processes

TL;DR

• Own production‑grade Machine Learning Operations (MLOps) platforms powering fraud and risk intelligence
• Decide how Machine Learning (ML) models are promoted, rolled back, and governed in production
• Build secure, auditable platforms across Amazon Web Services (AWS) and hybrid environments
• Partner with Data Scientists to turn models into reliable, explainable scoring services

Why This Role Matters

• Fraud models only create value when they are stable, explainable, and production‑ready
• This role governs the boundary between ML innovation and real‑world financial impact
• Engineering decisions here directly affect system resilience and regulatory confidence
• You enable PayNet to scale Artificial Intelligence (AI) without compromising trust

What You Will Actually Do

• Own end‑to‑end MLOps productionisation for fraud and risk intelligence use cases
• Build and operate Continuous Integration / Continuous Deployment (CI/CD) pipelines for model and service release
• Design and enforce model lifecycle management, including versioning, retraining, and redeployment
• Architect and operate secure AWS and on‑premises hybrid infrastructure for ML platforms
• Implement platform standards using Infrastructure as Code (IaC), containerisation, and Identity and Access Management (IAM)
• Ensure deployments meet audit, security, and regulatory requirements without sacrificing stability

Examples of This Role in Practice

• Decide whether a fraud model can be safely promoted during elevated transaction risk
• Design rollback mechanisms when a real‑time scoring service degrades latency
• Convert experimental notebooks into governed, auditable production pipelines
• Balance model accuracy, infrastructure cost, and response time at national scale

What Will Help You Succeed

• Experience building and operating production ML systems, MLOps platforms, or large‑scale DevOps environments
• Strong proficiency in Python for ML pipelines, model packaging, automation, and service integration
• Deep hands‑on expertise with AWS architecture, including secure networking and high‑availability design
• Proven ability to design CI/CD pipelines for ML services with gated releases and controlled promotion
• Experience with IaC tools such as Terraform and container orchestration using Kubernetes and Helm
• Familiarity with distributed workloads (e.g. Apache Spark or Ray) and orchestration tools such as Apache Airflow or Prefect