AI Data Engineer: ML Pipelines, RAG & Vector Embeddings

Valiant Technologies LLC; Phoenix (AZ); On-site; USD 76,000 - 103,000; Full time

Job summary

Valiant Technologies LLC in Phoenix, AZ seeks an AI Data Engineer to design and maintain data infrastructure for AI/ML initiatives. You will build data pipelines, vector embedding workflows, and feature stores to support model training and deployment.

You will collaborate with ML engineers to ensure data quality, governance, and reproducibility, while optimizing pipelines for cost and performance in a modern cloud environment.

Qualifications

• Strong SQL with Python or Scala for pipeline development.
• Experience with a cloud data platform.
• Experience with orchestration tools (Airflow, dbt).
• Understanding of embedding models, vector search, and RAG patterns.
• Experience with unstructured data processing.

Responsibilities

• Design and build pipelines to ingest, clean, and transform structured and unstructured data 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

Data governance

Unstructured data processing

Tools

Airflow

dbt

Vector databases

Embedding models

Job description

Valiant Technologies LLC in Phoenix, AZ seeks an AI Data Engineer to design and maintain data infrastructure for AI/ML initiatives. You will build data pipelines, vector embedding workflows, and feature stores to support model training and deployment.

You will collaborate with ML engineers to ensure data quality, governance, and reproducibility, while optimizing pipelines for cost and performance in a modern cloud environment.