[Hiring] Data / Context Engineer @QTech

Role Description

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.

• 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.

Qualifications

• 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).

• 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.

Requirements

• Must be US Citizen or Green Card.
• 100% Remote - EST/CST/MST.
• 5 Month contract, extension likely.
• End client: Verizon.

Benefits

• 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.