AI Data Engineer: Pipelines, RAG & MLOps

Machine Learning Technologies, LLC; Phoenix (AZ); Vor Ort; USD 74.000 - 105.000; Vollzeit

Zusammenfassung

Machine Learning Technologies, LLC in Phoenix, AZ is seeking an AI Data Engineer to design and maintain the data infrastructure powering our AI/ML initiatives, including data pipelines for model training, retrieval-augmented generation (RAG), and production ML systems.

You will collaborate with ML engineers and data scientists to ensure data quality, governance, and provenance, optimize pipelines for cost and performance, and implement entitlements and access controls.

Qualifikationen

• Strong SQL and at least one of Python or Scala for pipelines.
• Experience with a cloud data platform and data lakehouse concepts.
• Familiarity with Airflow and dbt for orchestration.
• Understanding of embedding models, vector search, and RAG patterns.
• Experience processing unstructured data (documents, PDFs, logs).

Aufgaben

• Design and build pipelines to ingest, clean, and transform data for AI/ML use cases.
• Build vector embedding pipelines and integrate vector databases.
• Develop feature stores/pipelines for model training and inference.
• Implement data pipelines supporting RAG architectures and tagging.
• Collaborate with ML engineers and data scientists on data requirements.
• Ensure data lineage, quality, and governance across pipelines.
• Optimize pipelines for cost and performance (Dynamic Tables, streaming).
• Support MLOps/LLMOps: versioning datasets and tracking data usage.
• Implement entitlement/access controls for data access.

Kenntnisse

SQL

Python/Scala

Cloud data platform

Airflow

dbt

Embedding models

Vector search

RAG patterns

Unstructured data

Jobbeschreibung

Machine Learning Technologies, LLC in Phoenix, AZ is seeking an AI Data Engineer to design and maintain the data infrastructure powering our AI/ML initiatives, including data pipelines for model training, retrieval-augmented generation (RAG), and production ML systems.

You will collaborate with ML engineers and data scientists to ensure data quality, governance, and provenance, optimize pipelines for cost and performance, and implement entitlements and access controls.