AI Data Engineer

Job Description

Position: AI Data Engineer

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Duration: 12+ Months Contract

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

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

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Job Description:

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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.

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

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Key Responsibilities:

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

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

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