AI Data Engineer: Scalable Pipelines, Embeddings & MLOps

MACHINE LEARNING TECHNOLOGIES LLC; Atlanta (GA); On-site; USD 120,000 - 180,000; Full time

Job summary

MACHINE LEARNING TECHNOLOGIES LLC in Atlanta is hiring an AI Data Engineer to design and maintain scalable data pipelines for AI/ML initiatives. The role focuses on SQL and Python/Scala, cloud data platforms, and RAG architectures, embeddings, and unstructured data processing.

Key responsibilities include building vector pipelines, feature stores for model training and inference, and ensuring data quality and governance across pipelines.

Qualifications

• Strong SQL and Python/Scala skills.
• 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

Cloud platforms

Vector embeddings

RAG architectures

Data pipelines

Tools

Apache Airflow

dbt

Vector databases

Job description

MACHINE LEARNING TECHNOLOGIES LLC in Atlanta is hiring an AI Data Engineer to design and maintain scalable data pipelines for AI/ML initiatives. The role focuses on SQL and Python/Scala, cloud data platforms, and RAG architectures, embeddings, and unstructured data processing.

Key responsibilities include building vector pipelines, feature stores for model training and inference, and ensuring data quality and governance across pipelines.