Artificial Intelligence Engineer

Role Purpose

Ai Model Development & Production Deployme

• nt:
• 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.

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

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) .

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.

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.

-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