AI Data Engineer: Pipelines, Vector Search & MLOps

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

Job summary

Robotics Technologies LLC is seeking an AI Data Engineer in Phoenix to design, build, and optimize data pipelines powering AI/ML initiatives. You’ll manage ingestion, cleaning, and transformation of structured and unstructured data for model training and deployment.

You will develop vector embedding pipelines, feature stores, and RAG-enabled architectures, ensuring data quality, lineage, and governance across systems.

Qualifications

• Proficiency in SQL and Python/Scala for pipeline development.
• Experience with a cloud data platform.
• Experience with orchestration tools such as Airflow and 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 (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

Airflow

dbt

Embedding models

Vector search

RAG patterns

Unstructured data

Job description

Robotics Technologies LLC is seeking an AI Data Engineer in Phoenix to design, build, and optimize data pipelines powering AI/ML initiatives. You’ll manage ingestion, cleaning, and transformation of structured and unstructured data for model training and deployment.

You will develop vector embedding pipelines, feature stores, and RAG-enabled architectures, ensuring data quality, lineage, and governance across systems.