AI Engineer, Assistant Manager (Evergreen)

DKSH; Kuala Lumpur; On-site; MYR 180,000 - 250,000; Full time

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.

Skills

Python programming

MLOps

Cloud platforms

Azure

CI/CD

Docker

Kubernetes

Databricks

MLFlow

Communication

Education

Bachelor's degree in Computer Science or related field

Tools

Databricks

MLFlow

Azure ML

Azure DevOps

Docker

Kubernetes

Job description

About The Role

The AI Engineer (Machine Learning Operations), Assistant Manager, Group Digital Transformation is an individual contributor role responsible for engineering, deploying, and operating production-grade Artificial Intelligence (AI) and Machine Learning (ML) solutions at scale across DKSH. This role sits at the intersection of AI, software engineering, and cloud platforms, enabling faster time-to-value from AI investments by establishing robust ML Operations (MLOps) practices and automation that power revenue-generating and mission-critical AI use cases globally.

What You Will Deliver

• Design, build, and maintain MLOps pipelines for training, testing, deploying, and monitoring AI/ML models in production, ensuring stable, reliable, and scalable performance.
• Engineer scalable model deployment architectures across cloud environments, including Microsoft Azure, to support both batch and real-time inference at enterprise scale.
• Implement automated workflows for model versioning, Continuous Integration and Continuous Deployment (CI/CD), rollback, and end-to-end lifecycle management.
• Ensure production AI systems consistently meet requirements for performance, reliability, security, and cost efficiency.
• Monitor models for data drift, performance degradation, and operational issues, and implement effective remediation strategies to sustain business continuity.
• Partner closely with AI Specialists, data scientists, and data engineers to successfully productionize models and analytics solutions.
• Develop reusable components, frameworks, and templates that accelerate AI delivery across markets and use cases.
• Integrate AI models into enterprise systems, digital products, and business workflows via Application Programming Interfaces (APIs) and services.
• Support the deployment of generative AI and Large Language Model (LLM)-based solutions with appropriate guardrails, observability, and operational controls.
• Define and enforce MLOps standards, best practices, and reference architectures to drive AI engineering maturity across DKSH.
• Contribute to documentation, runbooks, and knowledge sharing to uplift AI engineering capability across teams.
• Support audits, compliance, and responsible AI requirements from an engineering and operational perspective.
• Administrative duties and coordination tasks as required.

What You Bring

• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field; relevant cloud, DevOps, or AI engineering certifications are an advantage.
• Minimum 5 years of experience in AI engineering, MLOps, DevOps, or software engineering roles, with demonstrated experience supporting production AI or data-driven systems at scale.
• 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.
• Practical experience with CI/CD pipelines and automation for AI/ML workloads.
• Experience with cloud platforms, preferably Microsoft Azure, including Azure Machine Learning (Azure ML), Azure DevOps, and container technologies.
• Strong understanding of the machine learning lifecycle, encompassing training, inference, monitoring, and retraining.
• Experience with containerization and orchestration tools such as Docker and Kubernetes.
• Familiarity with infrastructure-as-code and platform automation practices.
• Exposure to generative AI and Large Language Model (LLM) deployment patterns.
• Experience working in agile or product-oriented delivery teams.
• Ability to engineer reliable, secure, and scalable systems in complex enterprise environments.
• Strong problem-solving mindset with close attention to operational detail.
• Ability to influence stakeholders and communicate effectively with both technical and non-technical audiences to drive cross-functional collaboration.

Why Join DKSH

At DKSH, we help companies grow in Asia and enable people to perform at their best. You will be part of an organization that values accountability, collaboration, and long-term partnerships. We offer a dynamic environment where your contributions are visible and where you can build a meaningful career in Digital Transformation.