AI / ML Engineer

1. Model Deployment & Productionization

• Deploy ML models into the Global Customer Platform.

• Ensure low-latency inference for real-time decisioning where required.

• Enable scalable batch scoring pipelines.

• Eliminate manual scoring processes through automation.

2. Pipeline Automation

• Build automated training and retraining workflows.

• Develop CI/CD pipelines for ML lifecycle management.

• Ensure consistent data refresh cycles aligned with SLA requirements.

• Reduce operational handoffs between Data Science and Engineering teams.

3. Model Monitoring & Governance

• Monitor model performance in production environments.

• Detect and mitigate model drift (data drift & concept drift).

• Track prediction accuracy, stability, and bias metrics.

• Maintain versioning and reproducibility standards.

4. Feature Engineering & Data Infrastructure

• Design and maintain feature stores.

• Ensure feature consistency between training and inference environments.

• Optimize data pipelines for reliability and scalability.

  • Collaborate with data engineering teams on data schema and quality controls.