AI Data Engineer: Pipelines, Vector DBs & LLM Ops

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

Job summary

Artifintech in Woodland, CA is seeking an experienced AI Data Engineer to build and maintain scalable data pipelines and infrastructure that support AI/ML, RAG, and large language model applications.

The role requires strong SQL and Python/Scala, hands-on experience with cloud data platforms, and expertise in embedding pipelines, vector databases, data governance, and ML lifecycle tooling to optimize performance, quality, and cost.

Qualifications

• Proven experience designing and building data pipelines for AI/ML workloads.
• Strong SQL and Python/Scala capabilities with production-grade code.
• Hands-on experience with cloud data platforms and governance.
• Experience with embedding pipelines, vector databases and RAG workflows.
• Familiarity with data lineage, quality controls and ML lifecycle tooling.

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

Airflow

dbt

AI/ML Data Engineering

RAG

Vector Databases

Embeddings

Vector Search

Unstructured Data Processing

ML Lifecycle Knowledge

Tools

Apache Airflow

dbt

Job description

Artifintech in Woodland, CA is seeking an experienced AI Data Engineer to build and maintain scalable data pipelines and infrastructure that support AI/ML, RAG, and large language model applications.

The role requires strong SQL and Python/Scala, hands-on experience with cloud data platforms, and expertise in embedding pipelines, vector databases, data governance, and ML lifecycle tooling to optimize performance, quality, and cost.