AI Engineer_GCP

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

Role Title: AI Engineer

Band / Level: 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.

Year of Experience - 7-12 years.

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 detectio
• 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

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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

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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

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