AI Data Engineer: Scalable ML Pipelines & Vector DBs

Ethereum Technologies; 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

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.