AI Engineer, Assistant Manager (Evergreen)

DKSH Malaysia Sdn Bhd; Kuala Lumpur; On-site; MYR 180,000 - 280,000; Full time

Job summary

DKSH Malaysia Sdn Bhd seeks an AI Engineer (MLOps) within Group Digital Transformation to design, deploy, and operate AI/ML solutions at scale. You will build robust MLOps pipelines, enable cloud-based deployment on Azure, and ensure reliable, secure AI systems across markets.

The role emphasizes collaboration with data scientists and engineers, with a focus on scalable, production-grade AI. As an individual contributor, you will implement CI/CD for AI workloads, monitor models for drift, and

Qualifications

• Bachelor's degree in CS/Engineering/Data Science or related field
• 5+ years in AI/ML engineering, MLOps, DevOps, or software engineering roles
• 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 technologies
• Strong understanding of the 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 model deployment architectures across cloud environments, including Microsoft 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, and implement remediation strategies.
• Collaborate with AI specialists, data scientists, and data engineers to productionize models and analytics solutions.
• Develop reusable components, frameworks, and templates to accelerate AI delivery across markets.
• Integrate AI models into enterprise systems via APIs and services.
• Support deployment of generative AI and LLM-based solutions with guardrails, observability, and controls.
• Define and enforce MLOps standards and reference architectures to mature AI engineering in the DKSH group.
• Contribute to documentation, runbooks, and knowledge sharing to uplift AI engineering capability.

Skills

MLOps

Python

Databricks

Azure ML

CI/CD

Docker

Kubernetes

Communication

Agile

Education

Bachelor's degree in CS/Engineering/Data Science

Tools

MLFlow

Azure DevOps

Job description

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.

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.

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.

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