Data / Context Engineer

๐Ÿข Glint Tech Solutions LLC ยท all Glint Tech Solutions LLC jobs
๐Ÿ“ United States
๐Ÿ“… Posted 2026-08-03 ยท via Himalayas
๐Ÿท Data-Engineering,Vector-Search-Engineer,Knowledge-Base-Engineering,Retrieval-Engineer,Data-Engineer,Data-Platform-Engineer,Data-+-AI-Engineer,Data-Infrastructure-Engineer,Data-And-AI-Engineer,Data-Engineering-Associate,Associate-Data-Engineer,AI-ML-Engineer
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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 โ€” 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

Originally posted on Himalayas

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