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# AI Data Engineer

- **Company:** Shrive Technologies Llc
- **Location:** Mosheim, TN, United States
- **Experience:** Starter
- **Salary:** $80,000.0 - $110,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Microsoft Azure, Continuous Integration, Data Cleansing, Data Dictionary, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Warehousing, DevOps, Data Flow Control, Python (Programming Language), Machine Learning, NoSQL, Search Technologies, SQL Databases, Unstructured Data, Informatica Powercenter, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Apache Spark, Generative AI, Git, Containerization, Data Lakes, Pyspark, Kubernetes, Deployment Automation, Star Schema, Data Management, Restful APIs, Data Pipelines, Databricks, Microservices
- **Published:** July 20, 2026
- **Apply:** https://www.careerjet.com/jobad/usd13fb52155739e0ed11a311d661ccd12

## About the Role

Mandatory Skills- Python, AI/ML
Retail domain experience is Required.
JD:
We are seeking an experienced AI Data Engineer (15+ Years) to design, develop, and manage scalable data platforms that enable advanced analytics, Machine Learning (ML), and Generative AI solutions. The ideal candidate will build robust data pipelines, ensure data quality, and integrate AI/ML capabilities into enterprise data ecosystems., * Python, SQL, PySpark

 * ETL/ELT development
 * Data Modeling (Star Schema, Snowflake Schema)
 * Apache Spark, Databricks
 * Airflow, Dataflow, Informatica, ADF, Synapse, or equivalent tools
 * Relational & NoSQL Databases
 * Data Warehousing concepts
 * REST APIs and Microservices
 * Git, CI/CD, DevOps practices

AI & GenAI Skills

 * Machine Learning fundamentals
 * Data preparation for AI models
 * Vector Databases (Pinecone, ChromaDB, FAISS)
 * LLM Integration (OpenAI, Azure OpenAI, Gemini, Claude, etc.)
 * RAG Architecture
 * Embeddings and Semantic Search
 * Prompt Engineering fundamentals

## Description

* Design, develop, and maintain scalable ETL/ELT pipelines for structured and unstructured data.
 * Build and optimize data lakes, data warehouses, and AI-ready data platforms.
 * Develop ingestion, transformation, and orchestration frameworks using cloud-native technologies.
 * Prepare, cleanse, and engineer datasets for AI/ML and Generative AI workloads.
 * Integrate Large Language Models (LLMs), vector databases, embeddings, and RAG (Retrieval-Augmented Generation) pipelines into enterprise solutions.
 * Implement data governance, security, lineage, and quality controls.
 * Collaborate with Data Scientists, AI Engineers, Business Analysts, and Solution Architects.
 * Monitor, troubleshoot, and optimize data pipelines and platform performance.
 * Automate deployment, testing, and monitoring of data engineering workflows.
 * Create technical documentation and data dictionaries for enterprise data assets.


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