AI Data Engineer

Ethereum Technologies LLC; Atlanta (GA); On-site; USD 120,000 - 160,000; Full time

Job summary

Ethereum Technologies is seeking an experienced AI Data Engineer to design, build, and maintain scalable data pipelines and infrastructure for AI/ML initiatives. The role emphasizes SQL and Python/Scala, cloud data platforms, and expertise in embedding models, vector databases, and RAG architectures.

You will design ingestion pipelines, vector embedding workflows, feature stores, and MLOps support, ensuring data quality, security, and cost efficiency across AI data pipelines.

Qualifications

• Strong SQL and Python/Scala proficiency.
• Experience with cloud data platforms.
• Hands-on with orchestration tools like Airflow or dbt.
• Understanding of embedding models, vector search and RAG architectures.
• Experience handling structured and unstructured data.

Responsibilities

• Design and develop data pipelines for ingesting, cleaning, and transforming data.
• Build and maintain vector embedding pipelines and integrate vector databases.
• Develop data pipelines and feature stores for ML training and inference.
• Implement RAG pipelines including document chunking 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 pipelines for performance, scalability, and cost efficiency.
• Support MLOps/LLMOps workflows, including dataset versioning and reproducibility.
• Implement data access controls and entitlement logic for secure AI data usage.

Skills

SQL

Python/Scala

Cloud data platforms

Apache Airflow

dbt

Vector embeddings

RAG architectures

Structured data processing

Unstructured data processing

ML lifecycle concepts

Tools

Apache Airflow

dbt

Job description

US Citizen, Green Card, H-1B, OPT-EAD, CPT, Other

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.