: Full Stack AI Engineer Claims Intelligence & MLOps

Experience Level: 4- 10 Years

Location: Hybrid - Gurugram/ Bangalore

About the Role

We are seeking a Full Stack AI Engineer to drive end-to-end architecture, development, and deployment for our AI-powered Claims Intelligence platform. In this expanded role, you will bridge machine learning pipelines, production MLOps deployment, robust backend services, and interactive user interfaces. You will operationalize deep NLP/LLM models, design data workflows, and deliver complete, client-facing web applications.

Key Responsibilities

- Full Stack & Application Development:
- Build responsive, scalable web interfaces using React or Angular.
- Develop robust API services and microservices using Node.js, JavaScript / TypeScript, and Python (FastAPI / Flask).
- Build intuitive workflows, dashboards, and review tools for underwriters, adjusters, and portfolio managers.
- MLOps & Production Model Deployment:
- Package, version, deploy, and monitor machine learning models in production using MLflow and Databricks.
- Build real-time and batch model inference endpoints deployed via containerized environments (Docker, Kubernetes / AWS EKS).
- Implement automated CI/CD pipelines, model drift monitoring, and evaluation tracking.
- AI Module Integration & NLP:
- Operationalize and connect NLP modules: entity extraction, document classification, de-identification (DEID), and PII masking.




- Implement hybrid search pipelines using GTE multilingual embeddings and vector search (pgvector / Aurora PostgreSQL).
- Integrate LLM APIs (OpenAI / open-source models) for multilingual summarization, query expansion, and trend detection.
- Data Pipelines & Cloud Infrastructure:
- Author and maintain batch and streaming workflows using Apache Airflow DAGs.
- Manage AWS cloud services including S3, Athena, Glue, Fargate, EKS, SQS, and Step Functions.
- Design ingestion pipelines parsing nested JSON, PDF attachments, and multilingual unstructured document data.

Required Skills & Experience

Frontend

React, Angular, JavaScript (ES6+), TypeScript, HTML5/CSS3

Backend

Node.js, Express/NestJS, Python, REST APIs, GraphQL, WebSockets

MLOps & Deployment

MLflow, Databricks, Docker, AWS EKS, CI/CD pipelines, Model Monitoring

AI / NLP & Search

LLM APIs (OpenAI), RAG architectures, GTE embeddings, pgvector, hybrid search, NER

Data Pipelines

Apache Airflow, batch processing, data orchestration, JSON/unstructured parsing

Cloud & Databases

AWS (S3, EKS, Fargate, Glue, SQS), Aurora PostgreSQL, SQL

Nice to Have

- Experience building production RAG (Retrieval-Augmented Generation) applications and evaluation harnesses.
- Exposure to financial services, insurance claims, or legal document processing.
- Familiarity with database schema migration tools like Liquibase.

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