Data & AI Engineer

RADcube - A NLogix Company

  • Indianapolis, IN
  • 4 days ago

    Highlights

    Required Technical Expertise: Python, SQL, data engineering, API integration, ETL/ELT, LLM integrations, RAG, agentic AI frameworks, structured and unstructured data processing, cloud services, CI/CD, and data governance. Summary: Develop and integrate the technical components that capture, store, process, and deliver business context to the client’s existing AI ecosystem.

    Numbers & Facts

    LocationIndianapolis, IN

    Description

    Data & AI Engineer
    Location:Indianapolis, IN

    Summary: Develop and integrate the technical components that capture, store, process, and deliver business context to the client’s existing AI ecosystem.

    Responsibilities:

    • Develop and maintain the Business Context Repository in a client approved environment, using the architecture and standards established for the project.
    • Build data integration pipelines to capture business context from approved sources, including metadata registries, catalogs, workshop outputs, BI reports, and usage information.
    • Implement structured storage and retrieval mechanisms for business definitions, ontology relationships, metadata, business rules, and ground truth datasets.
    • Integrate the repository with the client’s existing Context Injection component and relevant Graph, semantic, and metadata services.
    • Develop Python based automation to extract, transform, validate, and refresh business context.
    • Implement LLM assisted and agentic workflows for context drafting, enrichment, review, and maintenance, including human approval controls.
    • Build scheduled refresh processes, staleness detection, logging, and monitoring mechanisms.
    • Support development of the AI evaluation runner, execute technical integrations, troubleshoot failures, and maintain deployment configurations.
    • Deliver reusable source code, configuration, technical documentation, and operational runbooks to the client’s designated repositories.




    Requirements

    Required Technical Expertise: Python, SQL, data engineering, API integration, ETL/ELT, LLM integrations, RAG, agentic AI frameworks, structured and unstructured data processing, cloud services, CI/CD, and data governance.

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