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Machine Learning Engineer

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Model Deployment & Scaling

  • Productionise risk and fraud models developed by the DS team using robust, efficient, and maintainable architectures
  • Design low-latency, high-availability APIs and pipelines for real-time model inference.
  • Implement batch scoring systems for periodic risk assessments.=

MLOps & Infrastructure

  • Build and maintain CI/CD pipelines for model deployment and monitoring.
  • Set up automated feature engineering pipelines, leveraging feature stores.
  • Ensure model governance: reproducibility, versioning, auditability, and compliance with regulatory requirements.

Model Monitoring & Maintenance

  • Implement real-time and batch monitoring for data drift, concept drift, and model performance.
  • Build automated retraining workflows and model rollback mechanisms.

Collaboration with Risk DS

  • Work closely with risk data scientists to translate experimental code (Python, notebooks) into production-grade services.
  • Advise DS on efficient model architectures for operational environments.
  • Optimize feature computation for speed and scalability.

System Design & Integration

  • Integrate models with credit underwriting, fraud detection, collections, and merchant risk systems.
  • Collaborate with backend engineering to align on API contracts and system interfaces.

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