Senior AI Data Engineer: GraphRAG, Neo4j & LLM Platform

NDA; United States; Remote; USD 150,000 - 230,000; Full time

Job summary

NDA is building a greenfield AI-native platform to automate business processes using GraphRAG, knowledge graphs, MCP servers, and LLM orchestration. The role focuses on Python backend development, graph data modeling with Neo4j and Cosmos DB, and scalable services in FastAPI with Celery and message queues.

You will work on ML-enabled retrieval, agent technologies (LangChain/LangGraph), and cloud-native infra using Docker, Kubernetes, and CI/CD. Strong English communication is required.

Qualifications

• 5+ years of commercial Python development experience.
• Experience with graph databases (Neo4j, Azure Cosmos DB Gremlin API).
• Experience with AI/LLM systems (LangChain, LangGraph, RAG).
• Experience with MCP (Model Context Protocol) - servers and clients.
• Experience with backend development: FastAPI, Celery, PostgreSQL, Redis; experience with RabbitMQ and Kafka.

Responsibilities

• Design and implement graph data models, relationship mapping, and traversal logic using Cosmos DB (Gremlin API) and Neo4j.
• Optimize graph queries for performance at scale.
• Build GraphRAG solutions combining knowledge graphs, vector search, metadata, and LLM retrieval.
• Develop agentic AI systems with LangChain and LangGraph.
• Implement and host MCP servers/clients.
• Manage agent memory and spec-driven AI workflows.
• Integrate vector databases and semantic search platforms; develop pipelines and APIs in Python.

Skills

Python

Graph databases

Neo4j

Cosmos DB

AI/LLM

LangChain

LangGraph

MCP

FastAPI

Celery

PostgreSQL

Redis

RabbitMQ

Kafka

Docker

Kubernetes

CI/CD

English

Tools

Neo4j

Azure Cosmos DB Gremlin API

LangChain

LangGraph

MCP servers/clients

FastAPI

Celery

PostgreSQL

Redis

RabbitMQ

Kafka

Docker

Kubernetes

React

Next.js

Job description

NDA is building a greenfield AI-native platform to automate business processes using GraphRAG, knowledge graphs, MCP servers, and LLM orchestration. The role focuses on Python backend development, graph data modeling with Neo4j and Cosmos DB, and scalable services in FastAPI with Celery and message queues.

You will work on ML-enabled retrieval, agent technologies (LangChain/LangGraph), and cloud-native infra using Docker, Kubernetes, and CI/CD. Strong English communication is required.