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.
Deploy and Manage ML models on GCPs Vertex Al platform ensuring efficient and scalable execution.
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.
Pipeline Development:
Build and maintain robust pipelines for model training, tuning and deployment leveraging components of Vertex Al and GCP tooling like Cloud Composer utilizing Python and Java and Big Query.
Implement automated monitoring and alerting to track model performance and identify potential issues.
Develop and maintain data quality checks and validation including reconciliations in-line with Data Quality and Retention Controls.
Implement robust security measures to protect sensitive data and models.
ML Ops and Model Governance:
Establish and maintain best practices for ML Ops. Including version control, CI/CD pipelines and the Vertex Al Model Registry and End Points.
Implement MLOps tools to streamline model development, training, tuning, deployment, monitoring and explain.
Identify and address performance bottleneck in ML models and pipelines.
Troubleshoot and resolve ML issues ensuring optimal model performance and costs.
requirements-expected :
Strong proficiency in Python
Experience with GCP including Big Query, Cloud Composer and Vertex Al.
Proficiency in data engineering and pipeline development.
Experience of ML Ops principles and tools.
Strong problem-solving and analytical skills.
Experience with Java would be beneficial.
benefits :
sharing the costs of sports activities
private medical care
sharing the costs of professional training & courses
