Data / Context Engineer

RemoteRemoteFull TimePosted 7 days agoVisa sponsorship availableHidden Gem · Himalayas

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Required skills

RAG
retrieval
vector store
multi-tenant
multi-region
overlay semantics
inheritance semantics
Vertex AI Vector Search
Pinecone
Weaviate

Nice to have

promotion path
draft
approved
retired patterns
audit log
human-review workflows

Role summary

A multinational telecommunications technology company seeks a Data / Context Engineer to architect and scale a knowledge base (KB) system. This role involves shaping knowledge flow across a massive, multi-region operation, directly powering decision-making. You will work at the intersection of AI infrastructure, data engineering, and business impact, building and operating ingestion pipelines, implementing multi-regional architectures, and ensuring KB integrity. The position requires 4-6 years of hands-on RAG/retrieval/vector store engineering experience, including building multi-tenant or multi-region retrieval architectures. Familiarity with specific vector stores and embedding model evaluation is essential.

Job Title: Data / Context Engineer

Location: Remote

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

Project DescriptionA 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

Originally posted on Himalayas

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