AI Data Engineer: Pipelines, Vectors & LLM Ops

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

Job summary

Machine Learning Technologies, LLC in Woodland, CA is seeking an experienced AI Data Engineer to design scalable data pipelines and infrastructure supporting AI/ML, RAG, and LLM applications. The role requires strong SQL, Python/Scala, and cloud data platforms, plus experience with embedding pipelines and vector databases.

You will work on structured and unstructured data processing, data quality, governance, and access controls, while supporting ML training and MLOps workflows.

Qualifications

• Strong SQL and Python/Scala.
• Cloud Data Platforms.
• Apache Airflow / dbt.
• AI/ML Data Engineering.
• RAG, Vector Databases, Embeddings, and Vector Search.
• Unstructured Data Processing.
• ML Lifecycle Knowledge.

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 search

Unstructured data processing

ML lifecycle knowledge

Tools

Apache Airflow

dbt

Job description

Machine Learning Technologies, LLC in Woodland, CA is seeking an experienced AI Data Engineer to design scalable data pipelines and infrastructure supporting AI/ML, RAG, and LLM applications. The role requires strong SQL, Python/Scala, and cloud data platforms, plus experience with embedding pipelines and vector databases.

You will work on structured and unstructured data processing, data quality, governance, and access controls, while supporting ML training and MLOps workflows.