Artificial Intelligence Engineer
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Key skills for this role
About the Role
TestCrew is seeking an AI Engineer to design, build, and operationalize AI/ML solutions on Google Cloud Platform for a client's Data Office. You will develop production models for fraud detection, claims intelligence, and document OCR, using Vertex AI.
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 for FWA, OCR, and document classification
- 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 directly 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
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, data processing, and pipeline scripting
- Experience with GCP services: 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, multimodal models, RAG patterns
- Experience with Document Intelligence / OCR: Vertex AI Document AI, Tesseract, Layout Parser, multimodal LLMs
- Experience with NLP / Text Classification: text preprocessing, embedding models, classification fine tuning
- Experience with Fraud / Anomaly Detection: unsupervised methods, graph analytics, rule ensemble models
- Experience with Explainability / Responsible AI: SHAP, LIME, bias auditing, model cards
- Proficiency in SQL / BigQuery for feature engineering and training data preparation
- Experience with Git + CI/CD for model deployment pipelines 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 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.
Key Accountabilities
- AI Model Development & Production Deployment: Build, train, evaluate, and deploy production grade AI/ML models on GCP Vertex AI.
- 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.
- Evaluate and implement LLMs for OCR and FWA detection.
- MLOps & Model Lifecycle Management: Build and maintain MLOps infrastructure.
- Implement CI/CD for model deployment.
- Design and operate feature engineering pipelines.
- Maintain model cards for every production model.
- Cross Functional AI Solution Delivery (FWA & Claims): Work directly with FWA team.
- Deliver AI solutions for claims intelligence.
- Build pre authorization scoring models.
- Document Intelligence & OCR Engineering: Deploy and optimize Document OCR Extraction model.
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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