jobs in PayNet (Payments Network Malaysia)

全职 Principal MLOps Engineer 工作, 薪水, PayNet (Payments Network Malaysia) Federal Territory 公司招聘中 - Ricebowl

Principal MLOps Engineer

PayNet (Payments Network Malaysia)

Undisclosed

全职

KL City, Federal Territory

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工作地点

  • Jalan Sultan Mizan Zainal Abidin, Kompleks Kerajaan Kuala Lumpur Federal Territory Malaysia

职位描述

岗位职责

Why PayNet / Why Now

  • National payments infrastructure with real economic and systemic impact
  • Organisation entering a phase of greater scale, scrutiny, and performance expectations
  • Leadership demanding clearer differentiation, stronger governance, and better data
  • 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

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