AI Data Engineer: RAG Pipelines & Vector ML DataOps

Quantum Technologies. LLC; Dallas (TX); On-site; USD 120,000 - 180,000; Full time

Job summary

Quantum Technologies. LLC is seeking a Data Engineer to build and maintain data infrastructure for AI/ML initiatives, including data pipelines for model training, retrieval-augmented generation (RAG), and production ML systems.

You will design pipelines to ingest and transform data, develop embedding pipelines, feature stores, and data governance across AI systems, while optimizing for cost and performance and enabling MLOps workflows.

Qualifications

• Strong SQL + one of Python/Scala for pipeline development
• Experience with a cloud data platform
• Experience with orchestration tools (Airflow, dbt)
• Understanding of embedding models, vector search, and RAG patterns
• Experience with unstructured data processing
• Familiarity with ML lifecycle concepts

Responsibilities

• Design and build pipelines to ingest, clean, and transform structured and unstructured data (documents, PDFs, transcripts, logs) 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 supporting RAG architectures — chunking strategies, embedding refresh cycles, metadata tagging
• Partner with ML engineers/data scientists to understand data requirements for model training and evaluation
• Ensure data lineage, quality, and governance across pipelines feeding AI systems
• Optimize pipelines for cost and performance (e.g., Dynamic Tables, streaming ingestion)
• Support MLOps/LLMOps workflows — versioning datasets, tracking data used in model training for reproducibility and compliance
• Implement entitlement/access control logic so AI systems only access data they're authorized to use

Job description

Quantum Technologies. LLC is seeking a Data Engineer to build and maintain data infrastructure for AI/ML initiatives, including data pipelines for model training, retrieval-augmented generation (RAG), and production ML systems.

You will design pipelines to ingest and transform data, develop embedding pipelines, feature stores, and data governance across AI systems, while optimizing for cost and performance and enabling MLOps workflows.