AI Data Engineer: ML Data Pipelines & RAG Architect

Cloud Analytics Technologies, LLC; Dallas (TX); On-site; USD 90,000 - 130,000; Full time

Job summary

Cloud Analytics Technologies, LLC is seeking a Data Engineer to design and manage data pipelines powering AI/ML initiatives. You will ingest, clean, and transform data for traditional analytics and AI workloads, including vector embeddings and RAG architectures.

You will collaborate with ML engineers to define data requirements for training and evaluation, ensure data lineage and governance, and optimize pipelines for cost and performance.

Qualifications

• Strong SQL and Python/Scala for data pipelines.
• 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 data for AI/ML use cases.
• Build and maintain vector embedding pipelines and support vector DB integration.
• Develop feature stores/pipelines for ML model training and inference.
• Implement data pipelines supporting RAG architectures with metadata tagging.
• Partner with ML teams to understand data requirements for model training.
• Ensure data lineage, quality, and governance across AI pipelines.
• Optimize pipelines for cost and performance (e.g., Dynamic Tables, streaming).
• Support MLOps/LLMOps workflows — versioning datasets for reproducibility.

Skills

SQL

Python/Scala

Data engineering

Cloud data platform

Airflow

dbt

Embedding models

Vector search

RAG patterns

Unstructured data

ML lifecycle

Tools

Airflow

dbt

Job description

Cloud Analytics Technologies, LLC is seeking a Data Engineer to design and manage data pipelines powering AI/ML initiatives. You will ingest, clean, and transform data for traditional analytics and AI workloads, including vector embeddings and RAG architectures.

You will collaborate with ML engineers to define data requirements for training and evaluation, ensure data lineage and governance, and optimize pipelines for cost and performance.