AI Data Engineer

Position: AI Data Engineer

Duration: 12+ Months Contract

Locations: Scottsdale, AZ | La Vista, NE (Omaha) | Atlanta, GA

Work Location: Candidate must be based in one of the listed locations.

Job Description:

We are looking for an experienced AI Data Engineer to build and maintain data infrastructure supporting AI/ML initiatives, including data pipelines for model training, Retrieval-Augmented Generation (RAG), and production ML systems.

The role focuses on ensuring high-quality, governed, and accessible data for traditional analytics as well as AI-driven applications.

Key Responsibilities:

• Design and develop data pipelines to ingest, clean, and transform structured and unstructured data, including documents, PDFs, transcripts, and logs.
• Build and maintain vector embedding pipelines and integrate with vector databases.
• Develop feature stores and data pipelines for ML model training and inference.
• Implement data pipelines supporting RAG architectures, including chunking strategies, embedding refresh cycles, and metadata tagging.
• Collaborate with ML Engineers and Data Scientists to understand data requirements for model training and evaluation.
• Ensure data lineage, quality, and governance across pipelines supporting AI systems.
• Optimize data pipelines for cost and performance, including Dynamic Tables and streaming ingestion.
• Support MLOps/LLMOps workflows, including dataset versioning and tracking data used for model training to ensure reproducibility and compliance.
• Implement data entitlement and access control mechanisms to ensure AI systems access only authorized data.

Required Skills & Experience:

• Strong hands-on experience with SQL and at least one programming language: Python or Scala.
• Experience working with cloud-based data platforms.
• Hands-on experience with data orchestration tools such as Airflow and dbt.
• Understanding of embedding models, vector search, and RAG architectures.
• Experience processing structured and unstructured data.
• Familiarity with ML lifecycle concepts, including model training and inference.

Nice to Have:

• Experience with Snowflake Cortex, Cortex Search, or similar in-warehouse AI tools.
• Experience working in regulated industries, preferably financial services.
• Understanding of data entitlement, governance, and compliance requirements.
• Exposure to LLM application frameworks such as LangChain and LlamaIndex.