United Overseas Bank Limited (UOB) is a leading bank in Asia with a global network of more than 500 branches and offices in 19 countries and territories in Asia Pacific, Europe and North America. In Asia, we operate through our head office in Singapore and banking subsidiaries in China, Indonesia, Malaysia and Thailand, as well as branches and offices. Our history spans more than 80 years. Over this time, we have been guided by our values – Honorable, Enterprising, United and Committed. This means we always strive to do what is right, build for the future, work as one team and pursue long-term success. It is how we work, consistently, be it towards the company, our colleagues or our customers

We seek individuals with highly developed conceptual, strategic, and analytical skills, capable of striking a balance between visionary thinking and practical solutions. The ability to comprehend, inspire, and mobilize others is crucial. A business-oriented mindset coupled with effective storytelling will drive your success. We are looking for self-starters ready to take on responsibilities with enthusiasm.

What you are expected to do :

This MLOps / Integration Engineer role focuses on building and integrating Traditional AI, Generative AI, and Agentic AI solutions into enterprise environments. You will work closely with data scientists, architects, and DevOps teams to design, implement, and optimize AI pipelines and infrastructure.
Key Responsibilities:
Develop and maintain automation scripts using Linux shell scripting, Python, or other relevant tools.
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.

What qualifies you for the role :

Bachelor's degree in Computer Science, Engineering, or a related field.
Bash and Unix/Linux command-line toolkit is a must-have.
Hands-on experience with OpenShift, Docker, Kubernetes.
Knowledge of cloud platforms (e.g. AWS) is a must-have.
Exposure to data and network security and compliance in AI systems.
Knowledge of API integration and microservices architecture.
Proficiency in Python used both for automation and ML-related tasks
Knowledge of Workflow Orchestrator, such as Ctrl-M
Good knowledge of Logging and Monitoring tools, such as Splunk and Geneos.
Experience with Observability framework, such as Langfuse, Elastic Stack, Grafana, OpenTelemetry.
Understanding of Generative AI (e.g. prompt engineering, RAG pipelines) and Agentic AI concepts.

Machine Learning Ops Engineer