Role : AI Engineer (Python & GEN AI - GCP - LLM - RAG)

Exp : 7 - 12 years

Location : Gurugram/Bangalore/Pune

Employment Type : Full-time

We 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 pipelines

Nice 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