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Senior Machine Learning Engineer

Job description

​Senior Machine Learning Engineer 

A fantastic role working in the global markets team for one of the largest banks in Asia. In this role, you will work closely with the application and infrastructure teams and be responsible for productionalizing ML models developed by data scientists. This is a great role for an experienced machine learning engineer looking to get more exposure in the financial markets domain. 

The role offers a very competitive base salary plus bonus and benefits including insurance. 

Responsibilities

This role 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.

  • Have a strong relationship with the infrastructure and application development team in order to understand the best method of integrating the ML model into enterprise applications (e.g., transforming resulting models into APIs).

  • 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.

Technical Skills

  • Proficiency in Python used both for ML and automation tasks

  • Good knowledge of Bash and Unix/Linux command-line toolkit 

  • Hands on experience building CI/CD pipelines orchestration by Jenkins, GitLab CI, GitHub Actions

    or similar tools 

  • Knowledge of OpenShift / Kubernetes 

  • 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.

 If you are interested in this role or would like to have a confidential discussion, please click "Apply Now" or contact joel@tenten-partners.com

 

 

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