Artificial Intelligence Engineer

Role Purpose:

The AI Engineer designs, builds, and operationalizes AI and machine learning solutions across Client'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.

The AI Engineer will also evaluate and implement LLMs for OCR and FWA use cases to accelerate value delivery and build the team's frontier AI capability.

Key Accountabilities:

1-AI Model Development & Production Deployment:

•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 Overutilization, 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 LLMs for OCR and FWA detection - assess accuracy, latency, and cost; deploy on GCP.

2-MLOps & Model Lifecycle Management:

•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 BigQuery 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

3-Cross-Functional AI Solution Delivery (FWA & Claims):

•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 (< 2 seconds inference latency) .

4-Document Intelligence & OCR Engineering:

•Deploy and optimize the Document OCR Extraction model to GCP Vertex AI production .

•Build Document Classification pipeline: classify incoming insurance documents by type with high accuracy using Vertex AI.

•Evaluate multimodal capabilities for document understanding - compare against existing OCR models on accuracy, cost per document, and latency for Client’s document types.

•Integrate document intelligence outputs with downstream business workflows: pre-auth queue management, claims processing, and member enrollment - structured data flowing into BigQuery Gold layer.

5-Generative AI & Gemini Enablement:

•Lead evaluation and implementation of AI models across priority use cases - OCR, document classification, FWA detection, and member service intelligence.

•Design responsible AI guardrails for LLMs deployments - input validation, output filtering, hallucination detection, and audit logging for all generative AI interactions with business data.

•Build internal AI capability: run knowledge-sharing sessions with data engineers and analysts on LLMs APIs, Vertex AI generative AI.

6-AI Data Quality, Lineage & Ethics:

•Ensure all training datasets are sourced from the Gold layer - no model trained on uncleaned, ungoverned data from the legacy EDWH or raw SSIS extracts.

•Apply Client’s data classification standards to all model inputs and outputs - PII, PHI, and sensitive clinical data handled in compliance with PDPL and NDMO requirements.

•Conduct bias audits on FWA and clinical models - ensure models do not exhibit demographic bias and that outputs are explainable to clinical and compliance stakeholders.

Skills

•Vertex AI - Pipelines, Model Registry, Feature Store, Model Monitoring, online/batch inference

•Python - ML engineering (scikit-learn, XGBoost, TensorFlow, PyTorch), data processing, pipeline scripting

•GCP - BigQuery ML, BigQuery data access for training data, feature engineering queries

•MLOps - CI/CD for models, automated retraining, drift detection, model versioning

•LLMs API / Vertex AI Generative AI - prompt engineering, multimodal models, RAG patterns

•Document Intelligence / OCR - Vertex AI Document AI, Tesseract, Layout Parser, multimodal LLMs

•GCP - Pub/Sub, Dataflow, Cloud Functions for real-time AI inference pipelines

•NLP / Text Classification - text preprocessing, embedding models, classification fine-tuning

•Fraud / Anomaly Detection - unsupervised methods, graph analytics, rule-ensemble models

•Explainability / Responsible AI - SHAP, LIME, bias auditing, model cards

•SQL / BigQuery - feature engineering queries and training data preparation

•Git + CI/CD - model deployment pipelines and experiment tracking (MLflow or Vertex Experiments)

Education

• Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or related field.