AI Data Engineer: Vector Pipelines & MLOps

Quantum Technologies. LLC; Atlanta (GA); On-site; USD 120,000 - 180,000; Full time

Job summary

Quantum Technologies. LLC is seeking an experienced AI Data Engineer to design, develop, and maintain scalable data pipelines and infrastructure supporting AI/ML initiatives.

The role focuses on building vector embedding pipelines, integrating vector databases, and implementing RAG architectures while ensuring data quality, governance, and security across pipelines. You will collaborate with ML engineers and data scientists to support model training and evaluation, optimize performance and cost,

Qualifications

• Strong SQL and 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.

Responsibilities

• 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.

Skills

SQL

Python/Scala

RAG architectures

Embedding models

Vector databases

Unstructured data processing

Tools

Apache Airflow

dbt

Job description

Quantum Technologies. LLC is seeking an experienced AI Data Engineer to design, develop, and maintain scalable data pipelines and infrastructure supporting AI/ML initiatives.

The role focuses on building vector embedding pipelines, integrating vector databases, and implementing RAG architectures while ensuring data quality, governance, and security across pipelines. You will collaborate with ML engineers and data scientists to support model training and evaluation, optimize performance and cost,