AI Data Engineer: AI/ML Data Pipelines & RAG

Artifintech; Phoenix (AZ); On-site; USD 74,000 - 105,000; Full time

Job summary

Artifintech in Phoenix, AZ is seeking an AI Data Engineer to build and maintain the data infrastructure powering our AI/ML initiatives, including data pipelines for model training, RAG, and production ML systems. This role sits at the intersection of data engineering and applied AI to ensure high-quality data for analytics and AI consumption.

You will design ingest pipelines, vector embeddings, feature stores, and data governance, collaborating with ML engineers and data scientists to meet data

Qualifications

• Proficient in SQL and Python/Scala for pipelines.
• Experience with cloud data platforms and orchestration tools.
• Familiarity with embeddings, vector search, and RAG.

Responsibilities

• Design and build pipelines to ingest, clean, and transform structured and unstructured data (documents, PDFs, transcripts, logs) for AI/ML use cases.
• Build and maintain vector embedding pipelines and support vector database integration
• Develop feature stores/pipelines for ML model training and inference
• Implement data pipelines supporting RAG architectures — chunking strategies, embedding refresh cycles, metadata tagging
• Partner 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

Airflow

dbt

Vector embeddings

RAG patterns

Unstructured data

Tools

Vector DB

Job description

Artifintech in Phoenix, AZ is seeking an AI Data Engineer to build and maintain the data infrastructure powering our AI/ML initiatives, including data pipelines for model training, RAG, and production ML systems. This role sits at the intersection of data engineering and applied AI to ensure high-quality data for analytics and AI consumption.

You will design ingest pipelines, vector embeddings, feature stores, and data governance, collaborating with ML engineers and data scientists to meet data