AI Data Engineer: RAG, Pipelines & Vector DBs

Ethereum Technologies; Woodland (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Ethereum Technologies seeks an experienced AI Data Engineer to build scalable data pipelines and infrastructure for AI/ML, RAG, and LLM applications. You will leverage SQL, Python/Scala, cloud data platforms, and work with structured and unstructured data.

The role includes designing ingestion and transformation pipelines, embedding workflows, vector search, data quality and governance, and supporting MLOps. Collaboration across teams and cost-efficient, scalable solutions are expected.

Qualifications

• Strong SQL and Python/Scala development experience.
• Experience with cloud data platforms and data governance.
• Hands-on knowledge of Apache Airflow and dbt for data pipelines.
• Experience with AI/ML data engineering workflows.
• Familiarity with RAG, vector databases, embeddings and vector search.
• Ability to process structured and unstructured data (PDFs, logs, transcripts).
• Knowledge of ML lifecycle and MLOps concepts.

Responsibilities

• Design and develop data ingestion, transformation, and processing pipelines.
• Build embedding pipelines and integrate vector databases.
• Develop data pipelines supporting RAG, chunking, and vector search.
• Process structured and unstructured data (PDFs, documents, logs, transcripts).
• Implement data quality, governance, lineage, and access controls.
• Support ML model training, inference, MLOps, and LLMOps workflows.
• Optimize data pipelines for performance, scalability, and cost.

Skills

SQL

Python/Scala

Cloud Data Platforms

AI/ML Data Engineering

RAG/Vector Databases/Embeddings

Unstructured Data Processing

ML Lifecycle

Tools

Apache Airflow

dbt

Snowflake Cortex

Job description

Ethereum Technologies seeks an experienced AI Data Engineer to build scalable data pipelines and infrastructure for AI/ML, RAG, and LLM applications. You will leverage SQL, Python/Scala, cloud data platforms, and work with structured and unstructured data.

The role includes designing ingestion and transformation pipelines, embedding workflows, vector search, data quality and governance, and supporting MLOps. Collaboration across teams and cost-efficient, scalable solutions are expected.