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Manager, Machine Learning Engineer
2 weeks ago
**About UOB**:
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.
**About the Department**:
Group Technology and Operations (GTO) provides software and system development, information technology support services and banking operations.
We have centralized and standardized the technology components into Singapore, creating a global footprint which can be utilized for supporting our regional subsidiaries and the branches around the world. We operate and support 19 countries with this architecture to provide a secure and flexible banking infrastructure.
Our Operations divisions provide transactional customer services for our businesses while also focusing on cost efficiency through process improvements, automation and straight through processing.
**Responsibilities**:
You will be responsible for productionalizing ML models developed by data scientists. He/she will be the central point for ML models refactoring, optimization, containerization deployment and monitoring of its quality. Main responsibilities will include:
- Conduct reviews for compliance of the ML models in accordance with overall platform governance principles such as versioning, data / model lineage, code best practices and provide feedback to data scientists for potential improvements
- Develop pipelines for continuous operation, feedback and monitoring of ML models leveraging best practices from the CI/CD vertical within the MLOps domain. This can include monitoring for data drift, triggering model retraining and setting up rollbacks.
- Optimize AI development environments (development, testing, production) for usability, reliability and performance.
- Work with data engineers to ensure data storage (data warehouses or data lakes) and data pipelines feeding these repositories and the ML feature or data stores are working as intended.
- Evaluate open-source and AI/ML platforms and tools for feasibility of usage and integration from an infrastructure perspective. This also involves staying updated about the newest developments, patches and upgrades to the ML platforms in use by the data science teams.
**Job Requirements**:
**Technical Skills**
- Proficiency in Python used both for ML and automation tasks
- Good knowledge of Bash and Unix/Linux command-line toolkit is a must-have.
- Hands on experience building CI/CD pipelines orchestration by Jenkins, GitLab CI, GitHub Actions or similar tools is a must-have.
- Knowledge of OpenShift / Kubernetes is a must-have.
- Good understanding of ML libraries such as Panda, NumPy, H2O, or TensorFlow.
- Knowledge in the operationalization of Data Science projects (MLOps) using at least one of the popular frameworks or platforms (e.g., Kubeflow, AWS Sagemaker, Google AI Platform, Azure Machine Learning, DataRobot, Dataiku, H2O, or DKube).
- Knowledge of Distributed Data Processing framework, such as Spark, or Dask.
- Knowledge of Workflow Orchestrator, such as Airflow or Ctrl-M.
- Knowledge of Logging and Monitoring tools, such as Splunk and Geneos.
- Experience in defining the processes, standards, frameworks, prototypes and toolsets in support of AI and ML development, monitoring, testing and operationalization.
- Experience in ML operationalization and orchestration (MLOps) tools, techniques and platforms. This includes scaling delivery of models, managing and governing ML Models, and managing and scaling AI platforms.
- Knowledge of cloud platforms (e.g. AWS, GCP) would be an advantage.
**Soft Skills**
- Good knowledge of Devops process and principles
- Strong in Software Engineering fundamentals
- Excellent communication skills
- Attention to detail
- Analytical mind and problem-solving aptitude
- Strong Organizational skills
- Visual Thinking
**Be a part of UOB Family**:
