Senior Knowledge Engineer, Text Factory

Role Overview

• Drive the end to end migration of a legacy, rules-based text processing platform to a modern, Python based natural language processing (NLP) architecture, improving scalability, maintainability, and long term extensibility.
• Modernize legacy extraction logic.
• Analyze existing grammar, pattern rules, and linguistic logic from legacy systems and refactor them into contemporary NLP pipelines using industry standard libraries and frameworks.
• Design and implement context aware extraction.
• Build, test, and maintain high precision context extraction logic to identify entities, attributes, events, and relationships that support new product development and advanced analytics use cases
• Apply ontologies and taxonomies to text understanding.
• Develop, extend, and apply domain ontologies and taxonomies to standardize language interpretation, support semantic consistency, and improve the accuracy and explainability of extraction results.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field (or equivalent experience) is required.
• 7+ years of experience in data analysis, transformation, and development, with a strong specialization in Natural Language Processing in the insurance industry.
• 5+ years of strong proficiency in SQL and Python
• Strong background in natural language processing and text analytics, with hands on experience building rule-based and hybrid (rules + ML/AI) extraction solutions.
• Applied experience designing and using ontologies and taxonomies to model domain concepts, normalize language, and support semantic interpretation of unstructured text.
• Deep expertise in context-aware information extraction, including pattern matching, dependency-based rules, negation handling, section awareness, and linguistic feature engineering.
• Proficiency in Python software development, including building production ready libraries, writing maintainable code, and debugging complex text processing pipelines.
• Experience working with large volumes of unstructured or semi-structured text, such as documents, notes, forms, or free text fields.
• Ability to align extraction outputs to canonical domain models, controlled vocabularies, and taxonomy structures.
• Familiarity with extraction evaluation and tuning, including precision/recall measurement, error analysis, and iterative rule optimization.
• Understanding of data integration and pipeline concepts, including how extracted features are consumed by downstream analytics, ML models, or enterprise applications.
• Ability to translate evolving business and product requirements into precise, testable extraction logic.
• Experience supporting new product development, where requirements are iterative and domain models, terminology, and extraction strategies evolve over time.
• Strong communication skills to explain NLP behavior, limitations, and tradeoffs to both technical and non-technical stakeholders.
• Candidate must be authorized to work in the US without company sponsorship.

Tech Stack

• Python
• SQL

Benefits

• Short-term or annual bonuses
• Long-term incentives
• On-the-spot recognition