AI Data Engineer

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

Build and operate the data foundation behind AI and machine learning in Dallas, TX (onsite) for a contract role with 10 months + extension. In this position, you will design governed data pipelines and ML-ready datasets that enable model training, RAG, and production ML systems. You will also help ensure teams can trust what AI systems consume through data lineage, quality controls, and access entitlement logic.

Compensation: USD $80/hour (C2C). Job ID: J54343. Client: to be discussed later. Domain exposure: Oil & Gas. Employment type: W-2, 1099, or C2C. Work authorization: US Citizen, Green Card, OPT-EAD, CPT, H-1B, H4-EAD, Other.

What you’ll do

• Design and build pipelines that ingest, clean, and transform structured and unstructured data, including documents, PDFs, transcripts, and logs
• Create and maintain vector embedding pipelines and support vector database integration
• Develop feature stores and pipelines for ML training and inference
• Implement data pipelines for RAG architectures, including chunking strategies, embedding refresh cycles, and metadata tagging
• Work with ML engineers and data scientists to align data preparation with training and evaluation requirements
• Ensure data lineage, quality, and governance across pipelines that feed AI systems
• Optimize pipelines for cost and performance, including approaches such as Dynamic Tables and streaming ingestion
• Support MLOps/LLMOps workflows by versioning datasets and tracking data used in model training for reproducibility and compliance
• Implement entitlement and access control so AI systems only access data they are authorized to use

What you bring

• Strong SQL plus experience with Python or Scala for pipeline development
• Experience with a cloud data platform
• Hands-on experience with orchestration tools such as Airflow and dbt
• Understanding of embedding models, vector search, and RAG patterns
• Experience processing unstructured data
• Familiarity with ML lifecycle concepts

Tools and technologies

• SQL, Python, Scala
• Airflow, dbt
• Vector database, Dynamic Tables, streaming ingestion
• Snowflake Cortex, Cortex Search
• LangChain, Llama Index

Nice to have

• Experience with Snowflake Cortex, Cortex Search, or similar in-warehouse AI tooling
• Experience in a regulated industry with data entitlement and compliance requirements (financial services)
• Exposure to LLM application frameworks such as LangChain and Llama Index

Equal Opportunity Employer: All aspects of employment including hiring, promotion, discipline, or discharge are based on merit, competence, performance, and business needs.