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AI Data Engineer - Onsite (Charlotte NC)

NJTECH INC.Charlotte, NC🇺🇸United StatesPosted 3 Sept 2026

Why This Role Stands Out

This hybrid AI Data Engineer role offers significant career growth through hands-on experience with cutting-edge Generative AI and LLM technologies, allowing you to make a substantial impact on client success. You'll thrive here if you possess strong Python, SQL, and cloud platform skills, coupled with a passion for building robust data pipelines for AI applications. Embrace this exciting opportunity to advance your career in a dynamic tech environment.

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
Charlotte, NC, United States
Posted
17 hours ago
SQLAWSETLAzureGenerative AIGoogle CloudLLMPython

Job Description

We at NJ TECH INC are focused on hiring highly skilled professionals who are excited by the opportunity to make a true impact on their careers as well as on our clients' business. We power our clients success - and drive our consultants career growth.

We are seeking an experienced and outstanding AI Data Engineer for one of our esteemed clients.

Role: AI Data Engineer

Location: Charlotte NC

Duration : Long term

Required Skills

  • Strong experience in Data Engineering with hands-on Python development.
  • Strong SQL skills with experience in data modeling, data transformation, and data quality.
  • Experience building and maintaining ETL/ELT data pipelines.
  • Hands-on experience with Generative AI, LLMs, RAG, and AI/ML applications.
  • Experience working with OpenAI, Azure OpenAI, or other LLM platforms.
  • Experience with prompt engineering, embeddings, vector databases, and retrieval-augmented generation (RAG).
  • Experience integrating AI/LLM services with data platforms and enterprise applications.
  • Strong experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.

AI/GenAI Responsibilities

  • Design and develop data pipelines that support AI/ML and Generative AI applications.
  • Prepare and transform structured and unstructured data for LLM and AI use cases.
  • Build data ingestion pipelines for RAG and LLM applications.
  • Develop workflows for document processing, chunking, metadata extraction, embeddings, and vector search.
  • Integrate LLMs, vector databases, and enterprise data sources.

REGARDS

HAAS A

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