AI/ML Platform Engineer (MLOps) — Production at Scale

DKSH

Kuala Lumpur

On-site

MYR 180,000 - 250,000

Full time

12 hours ago

Be an early applicant

Application generator

Turn this role into an interview — a resume and cover letter built around what this employer wants.

Get past ATS filters

Job summary

DKSH is seeking an AI Engineer (MLOps) in Kuala Lumpur to design and manage production-grade AI/ML pipelines across cloud environments. You will engineer deployment architectures for batch and real-time inference, automate CI/CD, and ensure robust, secure, and cost-efficient AI services.

Collaboration with data scientists and engineers is essential to scale AI across markets. You will apply expertise in Python, Databricks, MLFlow, and Azure to maintain lifecycle management, monitor data drift,

Qualifications

Bachelor's degree in Computer Science, Engineering, Data Science, or a related field; cloud/AI engineering certifications are an advantage.
Minimum 5 years of experience in AI engineering, MLOps, DevOps, or software engineering with production AI or data-driven systems.
Strong hands-on experience in MLOps, AI engineering, or machine learning platform roles.
Proficiency in Python and software engineering best practices.
Hands-on experience with Databricks and MLFlow for model deployment and lifecycle management.
CI/CD pipelines and automation for AI/ML workloads.
Experience with cloud platforms, preferably Microsoft Azure, including Azure ML, Azure DevOps, and container tech.
Understanding of ML lifecycle: training, inference, monitoring, retraining.
Experience with Docker and Kubernetes.

Responsibilities

Design, build, and maintain MLOps pipelines for training, testing, deploying, and monitoring AI/ML models in production.
Engineer scalable deployment architectures across cloud environments, including Azure, for batch and real-time inference at enterprise scale.
Implement automated workflows for model versioning, CI/CD, rollback, and end-to-end lifecycle management.
Ensure production AI systems meet performance, reliability, security, and cost-efficiency requirements.
Monitor models for data drift and performance issues and implement remediation strategies.
Collaborate with AI specialists, data scientists, and data engineers to productionize models and analytics solutions.
Develop reusable components and templates to accelerate AI delivery across markets and use cases.
Integrate AI models into enterprise systems and APIs.