Role : AI Engineer (Python & GEN AI - GCP - LLM - RAG)Exp : 7 - 12 yearsLocation : Gurugram/Bangalore/PuneEmployment Type : Full-timeWe are looking for AI Engineers for Claims Intelligence & Data Pipelines work and GCP experience.What You Will Do :AI Module Integration & Inference Pipelines :- Integrate and adjust inference pipelines for NLP modules including document classification, entity extraction, de-identification (DEID), and LLM-based early trend detection.- Connect DS-coded AI modules into end-to-end production workflows via Airflow DAGs on AWS EKS.- Build and tune hybrid search pipelines combining GTE multilingual dense embeddings with GIN lexical search on Aurora PostgreSQL.- Integrate with OpenAI-based API platform for multilingual query expansion and LLM-driven trend detection.Document Processing & Parsing :- Design and maintain document preprocessing pipelines that parse deeply nested JSON structures (emails with attachments, embedded PDFs) from S3/DataLake.- Handle multilingual unstructured text (English, Spanish, Portuguese, German, Dutch, French, Italian) across 300 GB of claim notes and documents.- Build chunking strategies and metadata extraction for downstream embedding and retrieval workflows.Data Pipeline Engineering :- Author and maintain Airflow DAGs for batch processing (monthly entity refresh, trend detection,



DEID pipeline).- Manage data flow across AWS services: S3, Athena, Glue, Fargate, SQS, Step Functions.- Scale pipelines to handle 500K+ claims and hundreds of millions of text chunks.Production Deployment & Quality :- Deploy and version models using MLflow and Databricks.- Manage schema evolution and migrations using Liquibase on Aurora PostgreSQL.- Instrument pipelines with logging, monitoring, and evaluation scoring for retrieval quality.Required Skills :Area Skills :- Languages : Python (primary), SQL- AI / NLP : LLM API integration, multilingual embeddings (e.g., GTE), hybrid search, text classification, entity extraction, NER, PII masking- Data Pipelines : Apache Airflow, batch orchestration, large-scale unstructured data processing- Cloud & Infrastructure : GCP Preferred- Databases : PostgreSQL / Aurora, pgvector, GIN indexes, full-text search- ML Platform : MLflow, Databricks / Azure Databricks- DevOps : GitHub, CI/CD pipelinesNice to Have :- Experience with RAG (Retrieval-Augmented Generation) pipeline design and evaluation- Familiarity with insurance claims or financial services data- Experience with multilingual NLP at scale- Knowledge of Liquibase for database schema management- Exposure to Runway Model Catalog or similar model deployment platforms (ref:hirist.tech)

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