AI Data Engineer: ML Pipelines & Vector AI

Valiant Technologies LLC

Dallas (TX)

In loco

USD 120.000 - 170.000

Tempo pieno

3 ore fa
Candidati tra i primi

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Descrizione del lavoro

Valiant Technologies LLC is seeking a Data Engineer to design and maintain data infrastructure powering AI/ML initiatives, including data pipelines for model training, retrieval-augmented generation, and production ML systems across analytics and AI usage.

You will own ingestion and normalization of structured and unstructured data, develop vector embedding pipelines, feature stores, and RAG support, ensure data lineage and governance, optimize for cost and performance, and collaborate with ML

Competenze

  • Strong SQL and one of Python or 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.
  • Familiarity with ML lifecycle concepts.

Mansioni

  • Design and build pipelines to ingest, clean, and transform data 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 and 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.
  • Support MLOps/LLMOps workflows including dataset versioning and reproducibility.

Conoscenze

SQL
Python/Scala
Cloud data platform
Orchestration tools
Embedding models
Unstructured data processing
ML lifecycle concepts

Strumenti

Airflow
dbt
Snowflake Cortex

Descrizione del lavoro

Valiant Technologies LLC is seeking a Data Engineer to design and maintain data infrastructure powering AI/ML initiatives, including data pipelines for model training, retrieval-augmented generation, and production ML systems across analytics and AI usage.

You will own ingestion and normalization of structured and unstructured data, develop vector embedding pipelines, feature stores, and RAG support, ensure data lineage and governance, optimize for cost and performance, and collaborate with ML

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