Machine Learning Operations (MLOps) Engineer

Position Overview:

We are looking for a skilled Machine Learning Operations (MLOps) Engineer to join a leading bank in Asia. In this role, you will be responsible for the deployment, monitoring, and maintenance of machine learning models in production environments. You will work closely with data scientists, software engineers, and IT teams to ensure seamless integration and operational efficiency of ML solutions within our banking systems.

Key Responsibilities:

• Model Deployment: Develop and implement deployment strategies for machine learning models, ensuring scalability, reliability, and performance in production environments.
• Monitoring & Maintenance: Establish monitoring frameworks to track model performance, detect anomalies, and manage model drift, making necessary adjustments as required.
• Collaboration: Collaborate with data scientists and software engineers to streamline the transition of ML models from development to production.
• Automation: Automate the end-to-end machine learning workflow, including data preprocessing, model training, evaluation, and deployment pipelines using tools like CI/CD.
• Infrastructure Management: Manage and optimize cloud-based infrastructure and resources to support machine learning operations, ensuring cost-effectiveness and efficiency.
• Documentation: Maintain clear documentation of ML processes, model architectures, and operational procedures to ensure knowledge sharing and compliance.
• Continuous Improvement: Identify opportunities for process improvements and implement best practices in MLOps to enhance overall system performance.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
• 3+ years of experience in MLOps, DevOps, or related fields, preferably within the financial sector.
• Proficiency in programming languages such as Python, Java, or Scala, with experience in machine learning libraries (e.g., TensorFlow, PyTorch).
• Strong understanding of cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).
• Experience with CI/CD tools (e.g., Jenkins, GitLab CI) and data pipeline frameworks (e.g., Apache Airflow, Prefect).
• Familiarity with model monitoring and performance tracking tools (e.g., MLflow, Kubeflow).
• Excellent problem-solving skills and the ability to work collaboratively in cross-functional teams.

What We Offer:

• Competitive salary and comprehensive benefits package.
• Opportunities for professional development and career advancement.
• A collaborative and innovative work environment that values continuous improvement.
• The chance to make a significant impact in the financial industry through cutting-edge technology.