AI Data Engineer

Artifintech; Atlanta (GA); On-site; USD 120,000 - 180,000; Full time

Job summary

Artifintech, based in Atlanta, seeks an experienced AI Data Engineer to design, build, and scale data pipelines for AI/ML initiatives. You will work with SQL and Python/Scala, leverage cloud data platforms, and develop vector embedding pipelines and RAG workflows to support model training and inference.

Collaborate with ML Engineers and Data Scientists to ensure data quality, governance, and secure access across pipelines, while optimizing performance and cost.

Qualifications

• Proficient in SQL and Python/Scala for building data pipelines.
• Experience with cloud data platforms.
• Hands-on with Apache Airflow and dbt.
• Knowledge of embedding models, vector search, and RAG architectures.
• Experience processing structured and unstructured data.
• Understanding of ML lifecycle concepts and data engineering best practices.

Responsibilities

• Design and develop data pipelines to ingest, clean, and transform data.
• Build and maintain vector embedding pipelines and integrate vector databases.
• Develop data pipelines and feature stores for ML model training and inference.
• Implement RAG pipelines, including document chunking, embedding refresh, and metadata tagging.
• Collaborate with ML Engineers and Data Scientists to support model training and evaluation.
• Ensure data quality, lineage, governance, and security across AI data pipelines.
• Optimize data pipelines for performance, scalability, and cost efficiency.
• Support MLOps/LLMOps workflows, including dataset versioning and reproducibility.
• Implement data access controls and entitlement logic to ensure secure AI data usage.

Skills

SQL

Python/Scala

Cloud data platforms

Vector embeddings

RAG architectures

Structured/unstructured data

Data governance

Data quality

Tools

Apache Airflow

dbt

Job description

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

We are seeking an experienced AI Data Engineer to design, develop, and maintain scalable data pipelines and infrastructure supporting AI/ML initiatives. The ideal candidate will have strong SQL and Python/Scala skills, cloud data platform experience, and knowledge of RAG architectures, vector databases, embeddings, and unstructured data processing.

Key Responsibilities

Design and develop data pipelines to ingest, clean, and transform structured and unstructured data.

Build and maintain vector embedding pipelines and integrate vector databases.

Develop data pipelines and feature stores for ML model training and inference.

Implement RAG pipelines, including document chunking, embedding refresh, and metadata tagging.

Collaborate with ML Engineers and Data Scientists to support model training and evaluation.

Ensure data quality, lineage, governance, and security across AI data pipelines.

Optimize data pipelines for performance, scalability, and cost efficiency.

Support MLOps/LLMOps workflows, including dataset versioning and reproducibility.

Implement data access controls and entitlement logic to ensure secure AI data usage.

Required Skills

Strong SQL and proficiency in Python or Scala.

Experience with cloud data platforms.

Hands-on experience with Apache Airflow, dbt, or similar orchestration tools.

Understanding of embedding models, vector search, and RAG architectures.

Experience processing structured and unstructured data.

Knowledge of ML lifecycle concepts and data engineering best practices.

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.