NLP Engineer

ROLE SUMMARY

We are hiring a hands-on NLP Engineer to build robust pipelines that convert policy, regulatory, fintech, and healthcare documents into structured, graph-ready data. You will own the full extraction lifecycle from raw text to clean, schema-validated outputs using classical NLP, deep learning, and LLM APIs.

KEY RESPONSIBILITIES

• Pipeline Development:

Design and build end-to-end text extraction pipelines for policy, regulatory, fintech, and healthcare documents

• Entity \& Clause Extraction:

Extract key entities (countries, companies, minerals) and structure policy clauses and obligations

• Deep Learning \& Transformers:

Fine-tune BERT / RoBERTa for NER, text classification, and relation extraction tasks

• LLM Integration:

Leverage LLM APIs with structured output extraction, prompt engineering, and tool/function calling

• Data Engineering:

Build scalable Python pipelines for high-volume document processing with robust pre-processing for PDF, DOCX, and HTML

• Schema \& Graph Readiness:

Define and enforce JSON schemas; ensure outputs are clean and compatible with knowledge graph ingestion

• Accuracy Improvement:

Evaluate model performance, track metrics, and implement feedback loops to improve extraction quality over time

REQUIRED SKILLS

• 3–5 years hands-on NLP engineering real production pipelines, not just model experiments
• Strong Python skills: OOP, async programming, packaging, and testing
• NLP frameworks: spaCy, HuggingFace Transformers, NLTK
• Deep learning: fine-tuning transformer models for sequence labeling and classification
• LLM API integration: prompt engineering, structured outputs, and function/tool calling
• Data pipeline experience: ETL, batch processing, and text pre-processing at scale
• JSON schema design and validation using pydantic or json schema

GOOD TO HAVE

• Experience with legal, regulatory, or policy documents (contracts, compliance filings, government publications)
• Familiarity with knowledge graphs or graph databases (Neo4j, RDF)
• Document parsing tools: pdfplumber, Docling, Apache Tika
• Domain knowledge in fintech or healthcare NLP
• Exposure to information extraction benchmarks (CoNLL, DocRED, SciERC)