Manager - AI Engineering

Role

Purpose:

The AI Engineer designs, builds, and operationalizes AI and machine learning solutions across Bupa Arabia's Data Office and business functions. The role is cross‑functional by design — working across Fraud, Waste & Abuse (FWA) detection, claims intelligence, member analytics, and clinical scoring — deploying production AI solutions on Google Cloud Platform with Vertex AI as the primary engineering platform.

This is a hands‑on engineering role. The AI Engineer writes production code, builds automated ML pipelines, develops and deploys models to GCP inference endpoints, and collaborates with actuarial, clinical, and operational teams to translate business problems into reliable AI solutions. Prototypes in notebooks are not acceptable outcomes — every model must reach production with automated retraining, monitoring, and alerting.

Wave 1 priority use cases include Document OCR Extraction (100% built, awaiting deployment), FWA Service Over utilization, FWA Duplicated Claims, and FWA Provider Network Collusion. The AI Engineer will also evaluate and implement Gemini (approved for Bupa Arabia) for OCR and FWA use cases to accelerate value delivery and build the team's frontier AI capability.

• Build, train, evaluate, and deploy production‑grade AI/ML models on GCP Vertex AI — covering supervised, unsupervised, and generative AI approaches as required by the use case
• Deploy Wave 1 priority models to GCP production inference endpoints: FWA Service Over utilization, FWA Duplicated Claims, FWA Provider Network Collusion, Document OCR Extraction, Document Classification, and Service Code Mapping
• Implement automated model retraining pipelines using Vertex AI Pipelines — no model should require manual retraining once in production
• Evaluate and implement Gemini (approved by Bupa Arabia) for OCR and FWA detection — assess accuracy, latency, and cost; deploy on GCP

• Build and maintain MLOps infrastructure:
  Vertex AI Pipelines for training/evaluation, Vertex AI Model Registry for versioning, and Vertex AI Model Monitoring for drift detection
• Implement CI/CD for model deployment — models promoted from development to staging to production via automated pipelines with quality gates; no manual uploads
• Design and operate feature engineering pipelines: build and maintain the Vertex AI Feature Store from Gold layer Big Query data — features versioned, documented, and reusable across use cases
• Maintain model cards for every production model — documenting training data, evaluation metrics, known limitations, drift thresholds, and business use context

• Work directly with the FWA team to translate fraud patterns, clinical rules, and investigator knowledge into model features, labels, and evaluation criteria for FWA detection models
• Deliver AI solutions for claims intelligence: duplicate claims detection, service code mapping accuracy, and provider network anomaly detection — all with explainable outputs usable by claims adjusters
• Build pre‑authorization scoring models integrated with the operational Pre‑Auth workflow — providing real‑time risk scores to clinical teams within SLA (