AI Data Engineer - Pipelines, RAG & Vector Embeddings

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

Job summary

Quantum Technologies LLC in Phoenix is seeking an AI Data Engineer to build and maintain data infrastructure powering AI/ML efforts, including data pipelines for model training, retrieval-augmented generation, and production ML systems.

You will design ingest/transform pipelines for structured and unstructured data, develop vector embeddings, feature stores, and MLOps workflows, collaborating with ML engineers to ensure governance and cost efficiency.

Qualifications

• Strong SQL and Python/Scala for pipelines.
• Experience with cloud data platforms.
• Experience with orchestration tools (Airflow, dbt).
• Understanding of embedding models, vector search, and RAG.
• Experience processing unstructured data.

Responsibilities

• 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 model training and inference.
• Implement data pipelines supporting RAG architectures and tagging.
• Partner with ML engineers to meet data requirements for training/evaluation.
• Ensure data lineage, quality, and governance across pipelines.
• Optimize pipelines for cost and performance.
• Support MLOps workflows and data versioning for reproducibility.
• Implement entitlement/access control so AI systems access only authorized data.

Skills

SQL

Python/Scala

Cloud data platform

Airflow

Vector search / RAG

Unstructured data processing

Tools

Airflow

dbt

Job description

Quantum Technologies LLC in Phoenix is seeking an AI Data Engineer to build and maintain data infrastructure powering AI/ML efforts, including data pipelines for model training, retrieval-augmented generation, and production ML systems.

You will design ingest/transform pipelines for structured and unstructured data, develop vector embeddings, feature stores, and MLOps workflows, collaborating with ML engineers to ensure governance and cost efficiency.