Gen AI & MLOps Engineer | Cloud & OpenShift

United Overseas Bank

Kuala Lumpur

On-site

MYR 120,000 - 190,000

Full time

9 days ago

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Job summary

United Overseas Bank Malaysia Innovation Hub seeks an experienced AI/DevOps professional to join the Enterprise Analytics team. You will build and integrate AI solutions, design and optimize pipelines, and work with data scientists and engineers to deploy scalable AI workflows.

Requirements include Linux shell, OpenShift/Docker/Kubernetes, AWS, Python, API/microservices, and strong security/observability skills. This is a full-time on-site role in Kuala Lumpur with growth opportunities.

Qualifications

Bachelor's degree in Computer Science, Engineering, or related field.
Bash and Unix/Linux command-line toolkit is a must-have.
Hands-on experience supporting CI/CD pipelines (Bitbucket, Jenkins, Artifactory, Sonar, Veracode for SAST, JIRA workflow, and Confluence).
OpenShift, Docker and Kubernetes experience.
Cloud platforms (AWS) knowledge is essential.
Exposure to data and network security and compliance in AI systems.
API integration and microservices architecture knowledge.
Python for automation and ML tasks.
Workflow Orchestrator such as Ctrl-M.
Logging/Monitoring tools such as Splunk and OpenTelemetry.
Observability frameworks like Langfuse, Elastic Stack, Grafana.
Understanding of Generative AI and Agentic AI concepts.

Responsibilities

Focuses on building and integrating Traditional AI, Generative AI, and Agentic AI solutions into enterprise environments; collaborates with data scientists, architects, and DevOps teams to design, implement, and optimize AI pipelines and infrastructure.
Develop and maintain automation scripts using Linux shell scripting, Python, or other relevant tools.
Develop and maintain the entire DevOps pipelines.
Ensure seamless deployment and integration between cloud/prem environments (AWS).
Integrate AI models into production environments using containerized platforms such as OpenShift.
Implement and maintain network security protocols to safeguard AI systems and data pipelines.
Collaborate with cross-functional teams to understand AI workflows and translate them into robust engineering solutions.
Monitor and optimize system performance, reliability, and scalability.
Support CI/CD processes and infrastructure for AI model deployment and updates.