Machine Learning Engineer

AEON Bank; Kuala Lumpur; On-site; MYR 120,000 - 180,000; 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 work with Data Scientists, Data Engineers, Product Owners, and Software Engineers to transform models into scalable, secure services across our AI-driven platform. The role covers end-to-end lifecycle from data and model deployment to monitoring and continuous improvement.

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, AI, Software Engineering or related quantitative field.
• 2–3+ years of experience in a machine learning role or related data science/engineering experience.
• Strong understanding of ML algorithms, feature engineering and model evaluation.
• Familiarity with LLMs, RAG or AI agent frameworks (Langchain, Langgraph, Pydantic AI) is a plus.
• Proficiency in Python and scripting languages (bash).
• Experience building ML pipelines (Airflow, Metaflow), experiment tracking (MLflow) and CI/CD.

Responsibilities

• Design, build, and operate production-grade ML/AI systems.
• Collaborate with Data Scientists, Data Engineers, Product Owners and Software Engineers to scale models into services.
• Develop end-to-end MLOps pipelines including deployment, monitoring, and model versioning.
• Architect secure, scalable cloud-native AI infrastructure using IaC and platform engineering best practices.
• Establish monitoring and drift detection to ensure reliability and performance.

Skills

Python

ML Engineering

MLOps

Cloud (AWS/Azure/GCP)

Docker/Kubernetes

Education

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

Tools

Docker

Kubernetes

MLflow

Airflow

Terraform

Job description

As a Machine Learning Engineer, you will design, build, and operate production-grade ML and

AI systems that power intelligent experiences across our digital banking platform. You will work closely with Data Scientists, Data Engineers, Product Owners, and Software Engineers to transform machine learning and AI models into scalable, reliable, and secure production services. You will own the end-to-end machine learning and AI lifecycle, from data and model deployment, monitoring, optimization, to continuous improvement, while delivering measurable business value and exceptional customer experiences in our AI-driven digital banking platform.

Job Responsibilities

• ML Systems Engineering: Design, develop, maintain, and optimize machine learning systems, including batch, real-time, and streaming inference patterns.
• AI Engineering: Build agentic applications and experiences, including conversational agents, workflow and operations automation, agentic RAG, personalized action agents.
• MLOps: Design and implement end-to-end modular MLOps pipelines, including experiment tracking, model versioning, CI/CD, feature management, and automated deployment workflows.
• ML/AI Infrastructure: Design, provision, and optimize secure, scalable cloud-native AI infrastructure using Infrastructure as Code (IaC) and platform engineering best practices to support production ML/AI systems.
• Observability & Reliability: 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.

Job Requirements

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, or a related quantitative discipline.
• More than 2 - 3 years of experience in a machine learning role, or relevant experience in data science or engineering.
• Strong understanding of machine learning algorithms, feature engineering, and model evaluation. Experience working throughout the end-to-end machine learning lifecycle, including model deployment, monitoring, to continuous improvement.
• Familiarity with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) or AI agent frameworks (i.e. Langchain, Langgraph, Pydantic AI) is an advantage.
• Proficiency in Python and scripting languages like bash. Experience in engineering best practices (version control, testing, clean code principles, etc.)
• Experience building machine learning pipelines (e.g. Airflow, Metaflow), experiment tracking (MLflow), model versioning, CI/CD (e.g. Jenkins, Github Actions), and feature engineering workflows.
• Experience with containerization and orchestration technologies like Docker, Kubernetes, or equivalent.
• Familiarity with cloud-native applications on AWS, Azure, or GCP. Infrastructure as Code (Terraform, CloudFormation, etc.) experience is an added advantage.
• Strong analytical thinking, problem solving, and troubleshooting skills.
• Excellent communication and interpersonal skills, capable of effectively collaborating with diverse stakeholders from cross-functional agile teams.
• Agility and a proactive attitude towards embracing new technologies, contributing to the continuous innovation of our ML & AI capabilities.