AI Data Engineer — Pipelines for AI/ML & RAG

Ethereum Technologies; Dallas (TX); Vor Ort; USD 120.000 - 180.000; Vollzeit

Zusammenfassung

Ethereum Technologies in Dallas seeks a Data Engineer to design and maintain data infrastructure powering AI/ML initiatives, including data pipelines for model training and retrieval-augmented generation (RAG). This role sits at the intersection of data engineering and applied AI, ensuring high-quality, governed data for analytics and AI consumption.

You will collaborate with ML engineers and data scientists to meet data requirements for training and evaluation, while optimizing pipelines for

Qualifikationen

• Proven experience building data pipelines for AI/ML use cases.
• Strong SQL and Python/Scala programming for pipeline development.
• Experience with structured and unstructured data processing.
• Familiarity with ML lifecycle concepts and data governance.

Aufgaben

• Design and build pipelines to ingest, clean, and transform 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 for RAG architectures, including chunking and metadata tagging.
• Collaborate with ML engineers and data scientists to meet data requirements for model training and evaluation.
• Ensure data lineage, quality, and governance across pipelines feeding AI systems.
• Optimize pipelines for cost and performance (dynamic tables, streaming ingestion).
• Support MLOps/LLMOps workflows — track data used in model training for reproducibility.
• Implement entitlement/access control so AI systems access only authorized data.

Kenntnisse

SQL

Python

Scala

Cloud data platform

Airflow

dbt

Embeddings

RAG patterns

Unstructured data

ML lifecycle

Tools

dbt

Snowflake Cortex

Cortex Search

LangChain

Jobbeschreibung

Ethereum Technologies in Dallas seeks a Data Engineer to design and maintain data infrastructure powering AI/ML initiatives, including data pipelines for model training and retrieval-augmented generation (RAG). This role sits at the intersection of data engineering and applied AI, ensuring high-quality, governed data for analytics and AI consumption.

You will collaborate with ML engineers and data scientists to meet data requirements for training and evaluation, while optimizing pipelines for