• Bring your technical expertise and project management skills to lead small teams of researchers and engineers on advanced R&D projects funded by government and commercial customers. • Collaborate with external, well-known academic and industry researchers in Natural Language Processing, ML, AI, and computer vision. • Lead proposals for new funding from government R&D organizations and/or commercial companies. • Develop and evaluate algorithms to understand the textual content of textual and multi-modal data, and/or perform reasoning on extracted data. • Enjoy support and encouragement for participation in national and international NLP, machine learning, AI, and/or computer vision conferences.

• Develop robust solutions in the areas of Natural Language Processing, Large Language Models, Foundation Models, ML, and AI. • Develop and evaluate algorithms to understand the textual content of textual and multi-modal data, and/or perform reasoning on extracted data. • Enjoy support and encouragement for participation in national and international NLP, machine learning, AI, and/or computer vision conferences. • Be encouraged to seek funding to grow and develop your own research areas, if you desire.

• 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

Role Description 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. Qualifications - 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 Requirements - 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 Benefits - 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)