AI Data Engineer

Machine Learning Technologies, LLC; Phoenix (AZ); On-site; USD 74,000 - 105,000; Full time

Job summary

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.

Qualifications

• 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).

Responsibilities

• 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.

Skills

SQL

Python/Scala

Cloud data platform

Airflow

dbt

Embedding models

Vector search

RAG patterns

Unstructured data

Job description

US Citizen, Green Card, H-1B, OPT-EAD, CPT, Other

Job Title: AI Data Engineer
Location: Phoenix,AZ
Duration: 12 Months + Extension
Bill Rate: $65/hour
Job Type:W-2, 1099, C2C
Client: To Be Discussed Later
Work Authorization: US-Citizen, H-1B, OPT-EAD, GC-EAD

Job Description

We're looking for a Data Engineer to build and maintain the data infrastructure powering our AI/ML initiatives — including data pipelines for model training, retrieval-augmented generation (RAG), and production ML systems. This role sits at the intersection of data engineering and applied AI, ensuring high-quality, well-governed data is available for both traditional analytics and AI consumption.

Key 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

Required Skills/Experience:

• Strong SQL + one of Python/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

We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.