Artificial Intelligence Engineer
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Key skills for this role
About the Role
Design, build, and operationalize AI/ML solutions on Google Cloud Platform with Vertex AI. Develop production-grade models for fraud detection, claims intelligence, and document OCR.
Key Skills for This Role
Responsibilities
- Build, train, evaluate, and deploy production grade AI/ML models on GCP Vertex AI
- Deploy Wave 1 priority models to GCP production inference endpoints
- Implement automated model retraining pipelines using Vertex AI Pipelines
- Evaluate and implement LLMs for OCR and FWA detection
- Build and maintain MLOps infrastructure: Vertex AI Pipelines, Model Registry, Model Monitoring
- Implement CI/CD for model deployment with quality gates
- Design and operate feature engineering pipelines using Vertex AI Feature Store
- Maintain model cards for every production model
- Work with FWA team to translate fraud patterns into model features and evaluation criteria
- Deliver AI solutions for claims intelligence: duplicate claims detection, service code mapping, provider network anomaly detection
- Build pre authorization scoring models with real time risk scores
- Deploy and optimize Document OCR Extraction model to GCP Vertex AI production
Requirements
- Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or related field
- Experience with Vertex AI Pipelines, Model Registry, Feature Store, Model Monitoring
- Proficiency in Python for ML engineering (scikit learn, XGBoost, TensorFlow, PyTorch)
- Experience with GCP BigQuery ML, Pub/Sub, Dataflow, Cloud Functions
- Experience with MLOps CI/CD for models, automated retraining, drift detection
- Experience with LLMs API / Vertex AI Generative AI
- Experience with Document Intelligence / OCR (Vertex AI Document AI, Tesseract, etc.)
- Experience with NLP / Text Classification
- Experience with Fraud / Anomaly Detection methods
- Experience with Explainability / Responsible AI (SHAP, LIME, bias auditing)
- Proficiency in SQL / BigQuery for feature engineering
- Experience with Git + CI/CD and experiment tracking (MLflow or Vertex Experiments)
Full Job Posting
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, working across Fraud, Waste & Abuse (FWA) detection, claims intelligence, member analytics, and clinical scoring.
- Deploys production AI solutions on Google Cloud Platform with Vertex AI as the primary engineering platform.
Key Accountabilities
- Build, train, evaluate, and deploy production grade AI/ML models on GCP Vertex AI.
- Deploy Wave 1 priority models to GCP production inference endpoints.
- Implement automated model retraining pipelines using Vertex AI Pipelines.
- Evaluate and implement LLMs for OCR and FWA detection.
- Build and maintain MLOps infrastructure: Vertex AI Pipelines, Model Registry, Model Monitoring.
- Implement CI/CD for model deployment with quality gates.
- Design and operate feature engineering pipelines using Vertex AI Feature Store.
- Maintain model cards for every production model.
- Work with FWA team to translate fraud patterns into model features and evaluation criteria.
- Deliver AI solutions for claims intelligence: duplicate claims detection, service code mapping, provider network anomaly detection.
- Build pre authorization scoring models with real time risk scores.
- Deploy and optimize Document OCR Extraction model to GCP Vertex AI production.
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.
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