• 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)

• Help build out the NLU workflows and workstreams • Define and deliver data annotation pipelines and guidelines • Annotate and review linguistic data • Collect data and perform data analysis • Label text for disambiguation and text normalization • Evaluate current system outputs and provide solutions • Translation and localization tasks • Creating and evaluating training and test sets • Prompt engineering

• Lead or actively participate in all phases of flight testing for the Anura system • Improve robustness of offline speech-to-text engine for cluttered environments • Infer drone operator's intent and integrate with AI decision making • Support design and implementation of tactical dialog state tracking system