Title: AI Engineer, Assistant Manager (Evergreen)
Kuala Lumpur, MY, MY
About the Role
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