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# AI Data Engineer

- **Company:** Robotics Technologies LLC
- **Location:** Atlanta, GA, United States
- **Contract:** Temporary contract
- **Skills:** Artificial Intelligence, Airflow, Python (Programming Language), Machine Learning, Cloud Services, Standard Sql, DataOps, Search Technologies, Unstructured Data, Management of Software Versions, Workflow Management Systems, Feature Store, Cloud Platform System, Retrieval-Augmented Generation, Large Language Models, Usage Tracking, Machine Learning Operations, Data Pipelines
- **Published:** November 4, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=c08d51e95ae1fcd0

## About the Role

* Strong SQL and proficiency in Python or Scala.
 * Experience with cloud data platforms.
 * Hands-on experience with Apache Airflow, dbt, or similar orchestration tools.
 * Understanding of embedding models, vector search, and RAG architectures.
 * Experience processing structured and unstructured data.
 * Knowledge of ML lifecycle concepts and data engineering best practices.

## Description

We are seeking an experienced AI Data Engineer to design, develop, and maintain scalable data pipelines and infrastructure supporting AI/ML initiatives. The ideal candidate will have strong SQL and Python/Scala skills, cloud data platform experience, and knowledge of RAG architectures, vector databases, embeddings, and unstructured data processing., * Design and develop data pipelines to ingest, clean, and transform structured and unstructured data.

 * Build and maintain vector embedding pipelines and integrate vector databases.
 * Develop data pipelines and feature stores for ML model training and inference.
 * Implement RAG pipelines, including document chunking, embedding refresh, and metadata tagging.
 * Collaborate with ML Engineers and Data Scientists to support model training and evaluation.
 * Ensure data quality, lineage, governance, and security across AI data pipelines.
 * Optimize data pipelines for performance, scalability, and cost efficiency.
 * Support MLOps/LLMOps workflows, including dataset versioning and reproducibility.
 * Implement data access controls and entitlement logic to ensure secure AI data usage.