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Salary
≈ $96k – $183k per year (Estimated)
Location
Hybrid (United States)
Seniority
Middle · 4+ years exp

Confirmed on the employer's own hiring board on Oct 10, 2026. First seen by Alion on Oct 7, 2026.

Overview
Company
Impact
Profile match
Glint Tech Solutions is a women-owned IT staffing and recruiting firm that sources contract and direct-hire talent for enterprise clients, alongside an AI hiring product it markets as Glinto AI. Its postings cover client projects across the United States and Canada, from New York, Sunnyvale, Santa Clara and Reston to Toronto, and some defense roles require an active DoD Secret clearance. Open positions are mostly placements at client companies, including software and DevOps engineers, embedded and field application engineers, product managers, data center cooling sales staff and regulatory reporting analysts.

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
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