Machine Learning Engineer
24 hours ago
Our ideal candidate will have strong expertise in designing and developing robust data pipelines leveraging GCP's Vertex AI and Cloud Composer.
Responsibilities include:
- Designing and implementing data pipelines for model training, tuning, and deployment using Python and Java.
- Implementing automated monitoring and alerting to track model performance and identify potential issues.
- Maintaining data quality checks and validation including reconciliations in-line with Data Quality and Retention Controls.
- Ensuring robust security measures to protect sensitive data and models.
In addition to the above responsibilities, our Machine Learning Engineer - Data Pipeline Development will be expected to:
- Establish and maintain best practices for MLOps, including version control, CI/CD pipelines, and the Vertex AI Model Registry and End Points.
- Implement MLOps tools to streamline model development, training, tuning, deployment, monitoring, and explainability.
- Deploy and manage ML models on GCP's Vertex AI platform ensuring efficient and scalable execution.
- Identify and address performance bottlenecks in ML models and pipelines.
- Troubleshoot and resolve ML issues ensuring optimal model performance and costs.
- Work closely with Compliance Analytics data scientists to prepare and preprocess data for model training and evaluation.
- Assist in feature engineering and selection to ensure model performance.
- Develop techniques to visualize and explain model behavior ensuring model transparency and accountability in-line with PRA S51/23 guidelines.
- Collaborate with infrastructure and DevOps teams to establish efficient deployment and scaling strategies.
To be successful in this role, you will require:
- Strong proficiency in GCP, including BigQuery, Cloud Storage, and Vertex AI.
- Experience with Python and Java programming languages.
- Knowledge of CI/CD pipelines and MLOps tools.
- Ability to work effectively in a team environment.
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