Hire RAG Engineersin 2 weeks
BearPlex RAG engineers build production retrieval-augmented generation systems: citation-tracked, access-control-aware, multi-tenant. The specialists you need when generic RAG tutorials stop working.
What a RAG Engineer actually does at BearPlex
A RAG engineer at BearPlex specializes in production retrieval systems: the kind that handle real enterprise document corpora (10M+ documents), enforce role-based access control at retrieval time, integrate with existing IAM systems, track citations back to source paragraphs, and survive the daily reality of regulated industries. Generic RAG tutorials stop working at scale; production-grade RAG is its own discipline. Our RAG engineers know that chunking strategy matters more than people realize, that hybrid search (BM25 + vectors + reranking) consistently beats vector-only, that filter-first retrieval is how you enforce permissions, that RAGAS evaluation is non-negotiable. They've shipped systems handling millions of legal documents (with privilege preservation), enterprise knowledge bases (with org-chart-aware permissions), customer support corpora (with citation tracking), and regulated healthcare retrieval (with HIPAA boundary enforcement). They specialize in the production hardening that turns 'RAG demo' into 'RAG you trust with business-critical workflows.'
Sample engineer profiles
Anonymized to respect engineer privacy. Full bios shared under NDA during scoping.
Built a citation-tracked legal RAG over 4M+ documents for an AmLaw 100 firm: zero hallucination incidents in 18 months production.
Owns the BearPlex RAG eval framework: golden datasets across 11 active engagements, RAGAS faithfulness >90% threshold enforced.
Shipped GraphRAG for a Fortune 500 manufacturer's regulatory knowledge base: multi-hop reasoning over 50K interconnected documents.
Scaled a multi-tenant RAG system on pgvector + Postgres row-level security: 12K customer organizations, strict data isolation.
Skills matrix
The capabilities every BearPlex RAG Engineer brings on day one.
| Skill | Proficiency | Typical tools |
|---|---|---|
| Chunking strategy (semantic, structure-aware) | Expert | LangChain text splitters · Custom semantic chunkers · Document-structure parsing |
| Embedding model selection & evaluation | Expert | OpenAI text-embedding-3 · Cohere v4 · Voyage AI · BGE · Custom benchmarking |
| Vector database operations | Expert | Pinecone · Qdrant · Weaviate · pgvector · Milvus |
| Hybrid search (BM25 + vector + reranking) | Expert | BM25 implementations · Cohere Reranker · Cross-encoder rerankers |
| Access control enforcement (filter-first) | Expert | RBAC patterns · Postgres RLS · Vector DB metadata filtering |
| Citation tracking & verification | Expert | Anthropic Citations API · Custom provenance tracking |
| Evaluation (RAGAS, golden datasets, LLM-as-judge) | Expert | RAGAS · Custom golden datasets · LLM-as-judge harnesses |
| Document processing (OCR, structure extraction) | Advanced | Unstructured.io · Tesseract · Document AI services · Custom parsers |
| GraphRAG and knowledge graph integration | Advanced | LlamaIndex GraphRAG · Neo4j · Custom graph construction |
| Sovereign deployment & on-prem RAG | Advanced | Local embedding models · Sovereign vector DBs · Air-gapped deployment |
| Multi-tenant isolation patterns | Expert | Per-tenant indexes · Metadata partitioning · Row-level security |
| Observability for retrieval pipelines | Expert | LangSmith · Arize · OpenTelemetry · Custom retrieval dashboards |
How we vet RAG engineers
Technical screen
60-minute call covering production RAG experience, chunking strategy decisions, embedding model selection, hybrid search architecture. We're looking for engineers who can explain why their RAG system underperformed and how they fixed it.
Live coding
2-hour paired session building a small RAG pipeline with constraints (must handle access control, must implement evaluation, must have citation tracking). We watch for production thinking: observability, error handling, performance.
Systems design
90-minute design session on a production-realistic RAG system (e.g., 'multi-tenant RAG for a SaaS company with 10K organizations and strict data isolation'). We push on permissions, scale, evaluation strategy, and degradation modes.
Reference check + paid trial work
We talk to two prior managers or technical peers. The engineer then completes 1-2 days of paid sample work on a real BearPlex client engagement. Only if all four steps pass do they join the embedded pod.
What clients say
“Three vendors had built RAG prototypes for us. BearPlex's RAG engineer was the first to ship something we trusted in production. The citation tracking saved us from a malpractice incident in month two.”
“Our internal team had been iterating on RAG for nine months. BearPlex's engineer rebuilt the architecture in week three and we measured a 40-point accuracy improvement on our golden dataset.”
“The multi-tenant isolation pattern BearPlex's RAG engineer designed for us is now the foundation of three more product features. Best technical hire we've made this year.”
Hiring RAG engineers: questions answered
When your RAG project hits one of these signals: (1) corpus size exceeds 1M documents, (2) you have multi-tenant isolation requirements, (3) you need citation tracking for legal/medical/financial reasons, (4) generic RAG tutorials have stopped giving you accuracy gains. These are the moments where retrieval engineering becomes its own discipline.
Both. We work with whatever fits the engagement: closed models for managed-service simplicity, open models (Llama, Mistral) when sovereign deployment matters. The RAG architecture is largely model-agnostic; the engineer decides the model based on your constraints.
Most can: modern production RAG often involves agentic patterns (multi-step retrieval, query rewriting, agentic search). For systems that are primarily agentic with RAG as one component, our LLM engineers or AI engineers are typically a better fit. For systems that are primarily RAG-centric, our RAG engineers go deeper.
14 days from initial intake to embedded. Day 0 is a 60-minute scoping call. Days 1-7 we match an engineer based on your specific RAG challenges (legal/medical/finance domain, scale, sovereignty). Days 8-14 the engineer reads your codebase, sets up local dev, attends standups, and starts shipping by end of week 2.
21 days from start. If the engineer isn't a fit during the first 21 days, you don't pay for their time and we replace them at no cost. We've had to invoke this twice in 47 placements.
Most BearPlex RAG engagements run 6-12 months. The shortest is a 90-day War Room sprint to ship the production RAG system. Longer engagements (12+ months) typically expand from RAG into broader AI infrastructure work.
Yes. We work with whatever you've adopted: Pinecone, Qdrant, Weaviate, pgvector, Milvus, Elasticsearch. We push back when an architectural choice will hurt you in production, but we're not platform-aligned.
Primarily Lahore, Pakistan (HQ) with client-facing presence in Austin and Doha. Time zone overlap with US clients is 5-9 hours; we structure engagements with daily 2-3 hour overlap windows for synchronous work, async written handoff for the rest.
Sovereign deployment by default for sensitive corpora. Engineer works inside your VPC, your IAM, your storage. We sign NDAs and BAAs as required. We never train models on client data without explicit written agreement. Document access during engagement is audited.
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