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.

Ready to make an impact? Reach out to Elite Technical to learn more.

Required Skills

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.