Onsite AI Data Engineer - ML Pipelines & MLOps (Dallas)

Robotics Technologies; Dallas (TX); On-site; USD 91,000 - 129,000; Full time

Job summary

Robotics Technologies in Dallas, TX (onsite) seeks a data engineering professional for a 10-month contract with potential extension. You will design governed data pipelines and ML-ready datasets enabling model training, RAG, and production ML systems.

You will implement data lineage, quality controls, and access entitlement logic to ensure trusted AI data, while optimizing for cost and performance with Dynamic Tables and streaming ingestion.

Qualifications

• Proficiency in SQL and Python/Scala for data pipelines.
• Experience with a cloud data platform and orchestration tools.
• Hands-on with embedding/vector tech and ML data prep.

Responsibilities

• Design and build data pipelines ingesting structured and unstructured data.
• Develop vector embedding pipelines and support vector DB integration.
• Create feature stores and pipelines for ML training and inference.
• Implement data pipelines for RAG architectures and tagging.
• Collaborate with ML teams to align data prep with training needs.
• Ensure data lineage, quality, governance across AI data feeds.
• Optimize pipelines for cost and performance, incl. dynamic tables.
• Support MLOps/LLMOps workflows and data versioning for reproducibility.
• Implement entitlement and access controls for data usage.

Skills

Strong SQL

Python

Scala

Cloud data platform

Tools

Airflow

dbt

Vector database

Dynamic Tables

streaming ingestion

Snowflake Cortex

Cortex Search

LangChain

Llama Index

Job description

Robotics Technologies in Dallas, TX (onsite) seeks a data engineering professional for a 10-month contract with potential extension. You will design governed data pipelines and ML-ready datasets enabling model training, RAG, and production ML systems.

You will implement data lineage, quality controls, and access entitlement logic to ensure trusted AI data, while optimizing for cost and performance with Dynamic Tables and streaming ingestion.