Remote Data & Context Engineer for Global Knowledge Base

Glint Tech Solutions; Texas; Remote; USD 120,000 - 180,000; Full time

Job summary

Glint Tech Solutions is seeking a Data / Context Engineer for a remote role supporting a multinational telecom client. You will architect and scale a knowledge base system powering decision-making across 12 regions, integrating AI infrastructure with data pipelines.

Responsibilities include building multi-regional KB architecture, ingestion pipelines, regional overlays, and ongoing KB integrity and health reporting; strong emphasis on RAG/vector stores and tenancy semantics.

Qualifications

• 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.
• Knowledge of embedding models evaluation and retrieval quality metrics (Recall@k, Precision@k, MRR).
• Proficiency in Python and structured-doc ingestion pipelines (PDF/XML/spreadsheet chunks).

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 with sampled human approval gate.
• Add regional overlays during Sep-Oct using MOD-specific retrievable context.
• Own KB integrity checks, retrieval evaluation, and weekly health reporting.

Skills

RAG retrieval engineering

Multi-region architecture

Python

Embedding eval metrics

Vector search tech

Tools

Vertex AI Vector Search

Pinecone

Weaviate

pgvector tenancy

OpenSearch

Job 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