Data / Context Engineer

Glint Tech Solutions LLC

  • Texas
  • 3 days ago
  • Remote
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    Skills

    • Adobe Acrobatunmatched
    • Artificial Intelligence (AI)unmatched
    • Customer Support/Serviceunmatched
    • Embedded Systemsunmatched
    • Enterprise Applicationsunmatched
    • Knowledge Baseunmatched
    • Python Programming/Scripting Languageunmatched
    • Quality Metricsunmatched
    • Radio Frequencyunmatched
    • Spreadsheetsunmatched
    • Technical Recruitingunmatched
    • Telecommunications Industryunmatched
    • Telecommunications Standardsunmatched
    • XML (EXtensible Markup Language)unmatched

    Description

    Job Title: Data / Context Engineer

    Location: Remote

    Company Overview
    Glint Tech Solutions is a women-owned, global IT staffing and recruiting firm supporting enterprise clients across the USA and Canada.

    Project Description
    A leading enterprise client, a multinational telecommunications technology company, is seeking a Data / Context Engineer to play a pivotal role in shaping how knowledge flows across a massive, multi-region operation. This is a high-visibility opportunity to architect and scale a knowledge base (KB) system that will directly power decision-making across 12 regions and beyond. The role sits at the intersection of AI infrastructure, data engineering, and real-world business impact, with contributions visible across a nationwide operation from day one.

    Key Responsibilities

    • Sign and implement the multi-regional KB architecture in P1 alongside SA
    • Seed the KB across 12 regions during P2 (cohort 1 wk1, cohort 2 wk2, cohort 3 wk3 of July)
    • Build and operate the ingestion pipeline (machine-readable regional standards embeddings retrievable patterns) with sampled human approval gate
    • Add MOD-specific retrievable context as regional overlays during Sep-Oct
    • Own KB integrity checks, retrieval evaluation, and weekly health reporting

    Mandatory Skills

    • 4-6 years of hands-on RAG / retrieval / vector store engineering in production
    • Experience building a multi-tenant or multi-region retrieval architecture with overlay / inheritance semantics, not just "one big index"
    • Vertex AI Vector Search or transferable depth (Pinecone, Weaviate, pgvector with strong tenancy, OpenSearch hybrid)
    • Embedding model evaluation discipline — retrieval quality metrics (recall@k, precision@k, MRR), not vibes
    • Python; familiar with structured-doc ingestion pipelines (PDF / XML / spreadsheet chunked, normalized, embedded)

    Nice-to-Have Skills

    • Designed a promotion path between draft/approved/retired patterns with audit log
    • Sampled human-review workflows integrated with the writeback path
    • RF / telecom standards format familiarity

    Numbers & Facts

    LocationTexas (
    Remote
    )

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