Hire NLP Engineersin 2 weeks
BearPlex NLP engineers build production natural language systems: from classical NLP (named entity recognition, classification, parsing) through modern LLM-based pipelines (RAG, agents, fine-tuned models). They know when to use which tool and ship the right answer for the problem.
What a NLP Engineer actually does at BearPlex
An NLP engineer at BearPlex covers the full natural language processing stack: classical methods (regex, CRF, spaCy, transformers for classification), modern LLM-based methods (prompting, RAG, fine-tuning, agents), and the engineering work that turns NLP research into production systems. They've shipped: named entity recognition pipelines extracting structured data from unstructured text, document classification systems serving millions of inferences per day, RAG systems over millions of documents, multilingual NLP for global products, and conversational systems that combine intent classification with LLM generation. They know when to reach for a 7B-parameter LLM (most modern needs) vs when classical NLP is still the right answer (high-volume classification with strict latency budgets, multilingual entity extraction at scale). They build evaluation harnesses appropriate to NLP, not just LLM eval, but precision/recall on classification, span F1 on entity extraction, BLEU/ROUGE/BERTScore where appropriate.
Sample engineer profiles
Anonymized to respect engineer privacy. Full bios shared under NDA during scoping.
Built a clinical NER system for a US health system: extracts 40+ entity types from chart notes with 95%+ F1, deployed sovereign on-prem.
Designed the document understanding pipeline for a legal tech startup: handles 12 document types with structured extraction at 100K docs/day.
Shipped multilingual NLP for an ecommerce client: sentiment analysis and intent classification across 14 languages with consistent quality.
Migrated a financial-services classification pipeline from BERT to fine-tuned Mistral: 8 points accuracy lift, 40% lower per-call cost.
Skills matrix
The capabilities every BearPlex NLP Engineer brings on day one.
| Skill | Proficiency | Typical tools |
|---|---|---|
| Named entity recognition (NER) | Expert | spaCy · Hugging Face NER models · Stanza · custom CRF/BiLSTM |
| Text classification (intent, sentiment, topic) | Expert | Hugging Face Transformers · fastText · scikit-learn · fine-tuned LLMs |
| Information extraction from unstructured text | Expert | spaCy · LangChain extraction · instructor (Pydantic) · fine-tuned models |
| RAG pipelines for production | Expert | LlamaIndex · LangChain · Pinecone / Qdrant · Cohere Rerank |
| Multilingual NLP | Advanced | XLM-R · mBERT · Cohere Embed multilingual · language detection |
| Document understanding (PDF, layout, tables) | Advanced | Unstructured.io · LayoutLM · AWS Textract · Azure Document Intelligence |
| Fine-tuning small models for production | Expert | Hugging Face PEFT · Unsloth · TRL |
| Tokenization and text preprocessing | Expert | Hugging Face Tokenizers · tiktoken · SentencePiece |
| Evaluation for NLP tasks (P/R/F1, BLEU, span-level) | Expert | seqeval · Hugging Face evaluate · custom evaluators |
| Production NLP serving (sub-100ms latency) | Advanced | ONNX Runtime · Triton Inference Server · Hugging Face TGI |
| Knowledge graph extraction from text | Advanced | spaCy + custom extractors · Microsoft GraphRAG · REBEL |
| When to use classical NLP vs LLMs | Expert | benchmark-driven decisions, not religious takes |
How we vet NLP engineers
Technical screen
60-minute deep-dive on past NLP work. We probe tool selection (why this approach?), evaluation methodology (how did you know it worked?), and production behavior (what failed and why?). We screen out engineers who only know one toolkit: production NLP requires a wide tool palette.
Live NLP exercise
We give the candidate a realistic NLP problem (extraction or classification) on messy real-world data and 90 minutes. They must choose an approach, build a baseline, evaluate, and improve. We're looking for: pragmatic tool selection, rigorous evaluation, and good handling of dirty data.
Architecture interview
Whiteboard an NLP system for a realistic client scenario: multilingual document processing, 100K docs/day, 5 entity types, structured output, sub-second latency. We probe for tool selection, latency vs accuracy trade-offs, and operational thinking.
Reference checks + paid trial
Two engineering reference checks plus a 21-day paid trial on a real client engagement. We don't take engineers off trial until both Hamad and the client engineer report 'I want this person on the team next sprint.'
What clients say
“Their NLP engineer pushed back on our LLM-everywhere instinct and proposed a hybrid: spaCy NER for the high-volume cases, LLM only for hard ones. Cut our inference cost 8× and improved accuracy.”
“Senior-level NLP engineering is rare. The BearPlex engineer brought 8 years of production experience and shipped a clinical entity extractor that passed our medical informatics review.”
“We needed multilingual NLP done right. The engineer they sent had shipped production systems in 12 languages and knew the gotchas (tokenization, evaluation, model selection per language) cold.”
Hiring NLP engineers: questions answered
Yes: sub-100ms inference is routine for classical models on CPU; sub-500ms for transformer-based models on GPU. For LLM-based NLP needing low latency, we use smaller fine-tuned models, prompt caching, and parallel processing patterns. Latency engineering is part of the role.
Yes: we've shipped production NLP across English, Spanish, French, German, Japanese, Korean, Chinese, Hindi, Arabic, Portuguese, Italian, Dutch, Polish, Turkish, and others. Different languages have different tokenization, model availability, and evaluation considerations; our engineers know the per-language gotchas.
Yes: common engagement type. We use spaCy for high-volume English NER, Hugging Face transformer NER for higher-accuracy needs, and LLM-based extraction (with structured output via Pydantic / instructor) for complex multi-field extraction. We always build evaluation with span-level F1, not just exact-match accuracy.
Several techniques depending on the situation: (1) Few-shot LLM prompting works surprisingly well for languages with no fine-tuned models; (2) Multilingual pre-trained models (XLM-R, BGE-M3) provide reasonable baselines for many languages; (3) Active learning to bootstrap labeled data efficiently; (4) Fine-tuning multilingual models on small targeted datasets. We've made all of these work for client engagements in low-resource situations.
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 handoff for the rest.
Yes: common in our healthcare and legal engagements. We use Unstructured.io, LayoutLM, AWS Textract, and Azure Document Intelligence for document parsing, plus custom layouts when needed. For complex documents (contracts, clinical records, financial filings), we typically combine OCR with LLM-based structure extraction for the highest accuracy.
Yes: common cost-optimization pattern. We've replaced GPT-4 calls with fine-tuned 7B-parameter models in production (typically Mistral 7B or Llama 3.1 8B with LoRA); this typically achieves 90-95% of GPT-4 accuracy at 5-20× lower per-call cost. The investment pays back in months for high-volume workloads.
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