AI Data Engineer

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

Job summary

Machine Learning Technologies, LLC in Atlanta seeks an experienced AI Data Engineer to design, develop, and maintain scalable data pipelines supporting AI initiatives.

You will leverage SQL and Python/Scala, cloud data platforms, and orchestration tools like Airflow and dbt, with embedding models, vector databases, and RAG architectures to enable model training, evaluation, and MLOps.

Qualifications

• Experience designing data pipelines for structured and unstructured data.
• Strong SQL and Python/Scala proficiency.
• Cloud data platform experience required.
• Experience with Airflow or dbt.
• Familiarity with embeddings, vector databases and RAG architectures.
• Knowledge of ML lifecycle data engineering best practices.

Responsibilities

• Design and develop scalable data pipelines for AI/ML workloads.
• Build vector embedding pipelines and integrate vector databases.
• Develop data pipelines and feature stores for model training and inference.
• Implement RAG pipelines with document chunking and embedding refresh.
• Collaborate with ML engineers and data scientists to support training and evaluation.
• Ensure data quality, lineage, governance, and security across pipelines.
• Optimize pipelines for performance, scalability, and cost.
• Support MLOps/LLMOps workflows including dataset versioning and reproducibility.
• Implement access controls and entitlement logic for secure data usage.

Skills

SQL

Python

Scala

Cloud data platforms

Airflow

dbt

Embeddings

Vector databases

RAG architectures

Tools

Apache Airflow

dbt

Job 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.

Key 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.

Required Skills

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.

We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.