AI Data Engineer: Build ML Pipelines & RAG Systems

Ethereum Technologies; Phoenix (AZ); On-site; USD 74,000 - 105,000; Full time

Job summary

Ethereum Technologies in Phoenix seeks an AI Data Engineer to build and maintain data infrastructure powering AI/ML initiatives, including data pipelines for model training, RAG, and production ML systems.

You will design ingestion, transformation, and governance for structured and unstructured data, build vector embedding pipelines, and collaborate with ML teams to meet data requirements for training and evaluation. This role emphasizes data quality, lineage, and scalable pipelines.

Qualifications

• Strong SQL and one of Python/Scala for pipeline development.
• Experience with a cloud data platform.
• Experience with orchestration tools (Airflow, dbt).
• Understanding of embedding models, vector search, and RAG patterns.
• Experience with unstructured data processing.

Responsibilities

• Design and build pipelines to ingest, clean, and transform structured and unstructured data for AI/ML use cases.
• Build and maintain vector embedding pipelines and support vector database integration.
• Develop feature stores/pipelines for ML model training and inference.
• Implement data pipelines supporting RAG architectures—chunking, embedding refresh, metadata tagging.
• Partner with ML engineers and data scientists to understand data requirements for training and evaluation.
• Ensure data lineage, quality, and governance across pipelines feeding AI systems.
• Optimize pipelines for cost and performance (e.g., dynamic tables, streaming ingestion).
• Support MLOps/LLMOps workflows—tracking data used in training for reproducibility and compliance.
• Implement entitlement/access control logic so AI systems access only authorized data.

Skills

SQL

Python

Scala

Airflow

dbt

Vector embeddings

Unstructured data processing

Tools

Airflow

dbt

Job description

Ethereum Technologies in Phoenix seeks an AI Data Engineer to build and maintain data infrastructure powering AI/ML initiatives, including data pipelines for model training, RAG, and production ML systems.

You will design ingestion, transformation, and governance for structured and unstructured data, build vector embedding pipelines, and collaborate with ML teams to meet data requirements for training and evaluation. This role emphasizes data quality, lineage, and scalable pipelines.