ML Engineer, Production AI & MLOps

AEON Bank; Kuala Lumpur; On-site; MYR 120,000 - 200,000; Full time

Job summary

AEON Bank is seeking a Machine Learning Engineer to design, build, and operate production-grade ML and AI systems powering our digital banking platform. You will collaborate with data scientists, data engineers, product owners, and software engineers to transform models into scalable, reliable services.

You will own the end-to-end ML lifecycle—from data and model deployment, monitoring, and optimization to continuous improvement—delivering measurable business value and excellent customer

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, or a related quantitative discipline.

Responsibilities

• Design, develop, maintain, and optimize ML systems, including batch, real-time, and streaming inference patterns.
• Build agentic applications and experiences, including conversational agents, workflow and operations automation, agentic RAG, personalized action agents.
• Design and implement end-to-end modular MLOps pipelines, including experiment tracking, model versioning, CI/CD, feature management, and automated deployment workflows.
• Design, provision, and optimize secure, scalable cloud-native AI infrastructure using IaC and platform engineering best practices to support production ML/AI systems.
• Establish robust monitoring and drift detection systems to track functional and operational performances in real-time, actively identifying areas for optimisation to enhance scalability, efficiency, and reliability.

Skills

Python

ML lifecycle

MLOps

Docker

Kubernetes

Airflow

Metaflow

MLflow

CI/CD

Jenkins

Education

Bachelor's or Master's degree in CS/DS/AI/SE

Tools

Docker

Kubernetes

Airflow

Metaflow

MLflow

Jenkins

GitHub Actions

Terraform

CloudFormation

Job description

AEON Bank is seeking a Machine Learning Engineer to design, build, and operate production-grade ML and AI systems powering our digital banking platform. You will collaborate with data scientists, data engineers, product owners, and software engineers to transform models into scalable, reliable services.

You will own the end-to-end ML lifecycle—from data and model deployment, monitoring, and optimization to continuous improvement—delivering measurable business value and excellent customer