AI Data Engineer: Pipelines, RAG & Data Governance

MACHINE LEARNING TECHNOLOGIES LLC; Dallas (TX); On-site; USD 91,000 - 129,000; Full time

Job summary

MACHINE LEARNING TECHNOLOGIES LLC in Dallas, Texas is seeking an AI Data Engineer to design and maintain the data infrastructure powering its AI/ML initiatives. The role focuses on building data pipelines for model training, retrieval-augmented generation, and production ML systems.

You will collaborate with ML engineers and data scientists to ensure high-quality, governed data, optimize pipelines for cost and performance, and implement data access controls across teams.

Qualifications

• Experience designing and maintaining data pipelines for AI/ML use cases.
• Experience with embedding models, vector search, and RAG patterns.
• Strong SQL and Python/Scala for pipeline development.

Responsibilities

• Design and build data pipelines to ingest, clean, and transform structured and unstructured data for AI/ML use cases.
• Build and maintain vector embedding pipelines and integrate vector databases.
• Develop feature stores/pipelines for ML model training and inference.
• Implement data pipelines for RAG architectures — chunking strategies, embedding refresh cycles, metadata tagging.
• Collaborate 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.

Skills

SQL

Python/Scala

Cloud data platform

Vector embeddings

RAG patterns

Unstructured data processing

ML lifecycle concepts

Tools

Airflow

dbt

Snowflake Cortex

Job description

MACHINE LEARNING TECHNOLOGIES LLC in Dallas, Texas is seeking an AI Data Engineer to design and maintain the data infrastructure powering its AI/ML initiatives. The role focuses on building data pipelines for model training, retrieval-augmented generation, and production ML systems.

You will collaborate with ML engineers and data scientists to ensure high-quality, governed data, optimize pipelines for cost and performance, and implement data access controls across teams.