Site SME responsible for cleaning validation and verification activities in pharmaceutical manufacturing environments. Collaborating with departments to ensure compliance with regulatory requirements and implementing effective cleaning strategies.
About the role
- Sr Knowledge Engineer developing modern NLP architectures to enhance enterprise-grade AI products. Collaborating on legacy migrations and context-aware extraction for new product development.
Responsibilities
- 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.
Benefits
- Short-term or annual bonuses
- Long-term incentives
- On-the-spot recognition
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