Data/Context Engineer -Fully Remote

Seeking a Data / Context Engineer to join a leading multinational telecommunications technology company and 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 Verizon regions and beyond.

Responsibilities:
Sign and implement the multi-regional KB architecture in P1 alongside SA.
Seed the KB across 12 Verizon 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.

This position offers exposure to enterprise-scale KB architecture at a top-tier telecommunications company. You will work at the intersection of AI infrastructure, data engineering, and real-world business impact, with your contributions visible across a nationwide operation from day one.

Project Overview 

Project overview: the end-client has launched a strategic AI transformation initiative to automate portions of its RF tower design (RFDS) process by embedding AI agents within its newly developed RFDA platform. The solution will leverage agentic workflow orchestration to analyze engineering requests, execute RF simulations, generate optimized tower design recommendations, and support human-in-the-loop decision making, with a phased adoption model progressing from AI shadowing RF engineers to fully autonomous execution.

Required Skills:
4-6 yrs hands-on RAG / retrieval / vector store engineering in production.
Built 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, normalised, embedded).

Should-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.

Apply today for immediate consideration. I look forward to hearing from you.