Artificial Intelligence Engineer
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Key skills for this role
About the Role
We are seeking an AI Engineer to design, build, and operationalize AI/ML solutions on Google Cloud Platform using Vertex AI. The role involves developing production-grade models for fraud detection, claims intelligence, and document OCR, with a focus on MLOps and cross-functional delivery.
Key Skills for This Role
Responsibilities
- Build, train, evaluate, and deploy production grade AI/ML models on GCP Vertex AI
- 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
- Design and operate feature engineering pipelines using Vertex AI Feature Store
- Work with FWA team to translate fraud patterns into model features and evaluation criteria
- Deliver AI solutions for claims intelligence and pre authorization scoring
- Deploy and optimize Document OCR Extraction model to production
- Build Document Classification pipeline for insurance documents
- Lead evaluation and implementation of generative AI models with responsible AI guardrails
- Ensure training datasets are sourced from Gold layer and comply with data classification standards
- Conduct bias audits on FWA and clinical models
Requirements
- Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or related field
- Hands on 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 services including BigQuery ML, Pub/Sub, Dataflow, Cloud Functions
- Experience with MLOps: CI/CD for models, automated retraining, drift detection, model versioning
- Experience with LLMs API / Vertex AI Generative AI, prompt engineering, RAG patterns
- Experience with Document Intelligence / OCR (Vertex AI Document AI, Tesseract, Layout Parser)
- Knowledge of fraud/anomaly detection methods
- Experience with explainability tools (SHAP, LIME) and bias auditing
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 from Gold layer BigQuery data.
- Maintain model cards for every production model.
- Work directly with FWA team to translate fraud patterns into model features.
- Deliver AI solutions for claims intelligence and pre authorization scoring.
- Deploy and optimize Document OCR Extraction model.
- Build Document Classification pipeline for insurance documents.
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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