Natural Language Processing Engineer

The Principal NLP Scientist is a senior technical leader responsible for designing, researching, and improving advanced Natural Language Processing and Large Language Model capabilities for production business systems.This role combines applied research, hands-on model development, technical architecture, and practical product impact. The Principal NLP Scientist will lead the design of NLP solutions for named entity recognition, text classification, text generation, semantic search, information extraction, and other language-driven automation use cases.This is not only a research role. The focus is to take modern NLP and LLM technologies and make them reliable, measurable, maintainable, and useful inside real production workflows.Assigned Product GroupProduct Group | NLP / AI AutomationStream | Software Engineering / AI & Machine LearningRole Type | Principal-level individual contributor / technical leaderThe Principal NLP Scientist will work closely with software engineers, data engineers, product managers, analysts, and data annotation teams to define, build, evaluate, and continuously improve NLP models and language-based automation systems.Product Group Focus AreasThe NLP product group is responsible for building and improving systems related to:Named entity recognition and structured data extractionText classification and categorizationText generation and language-based automationLarge Language Model evaluation, adaptation, and integrationRetrieval-augmented generation and semantic searchKnowledge graph and GraphRAG-based approaches for connecting structured business data, unstructured text, and entity relationships in AI assistant workflowsData preparation, annotation strategy, and labeling qualityModel evaluation, monitoring, and production performanceApplied NLP research and prototype developmentIntegration of NLP models into internal business applicationsInsight on Your ImpactIn this role, you will influence how the company uses modern NLP and LLM technologies across internal platforms and operational workflows.You will define technical direction for NLP systems, evaluate new approaches, design experiments, create prototypes, and help move successful models into production. Your work will directly affect automation quality, data processing accuracy, operational efficiency, and the long-term AI capabilities of the company.The role requires strong scientific depth, but also practical engineering judgment. The right candidate should be able to read research papers, understand model architecture, design measurable experiments, and also work with engineers to make sure the final solution can run reliably in production.Your Qualifications, Your InfluenceTo be successful in this role, you should have:PhD in Computer Science, Machine Learning, Artificial Intelligence, Computational Linguistics, Applied Mathematics, Data Science, or a closely related technical field8+ years of professional experience in machine learning, artificial intelligence, or NLP5+ years of hands-on experience building NLP models for production or near-production systemsDeep understanding of modern neural network architectures, including RNN, CNN, Transformer-based architectures, attention mechanisms, embeddings, fine-tuning strategies, layers, modules, and loss functionsStrong practical experience with NLP tasks such as NER, classification, text generation, semantic similarity, information extraction, and document understandingStrong experience with Large Language Models, including model evaluation, prompt design, fine-tuning, retrieval-augmented generation, and safe production usagePractical understanding of RAG, GraphRAG, knowledge graphs, embeddings, and hybrid retrieval approaches for production LLM applicationsStrong hands-on experience with PythonStrong experience with PyTorch and Hugging Face TransformersExperience with ONNX or other model optimization / model serving formatsStrong understanding of data preparation, data quality, labeling workflows, annotation guidelines, and model evaluation metricsPractical experience with main data analysis and machine learning libraries, including Pandas, NumPy, SciPy, scikit-learn, and MatplotlibExperience working with SQL databases and structured business dataExperience with cloud platforms such as Microsoft Azure or AWSAbility to design experiments, define success metrics, compare modelapproaches, and explain trade-offs clearlyStrong written and verbal English communication skillsExperience working in Agile engineering environmentsAbility to provide technical leadership without requiring formal people management authorityPreferred Skills and Technical FamiliarityThe following experience will be helpful:Experience leading NLP or AI research initiatives in a commercial production environmentExperience with multilingual NLP systemsExperience with vector databases, embeddings, semantic search, and RAG architecturesExperience with knowledge graph concepts, including entity and relationship modeling, graph schema design, traversal queries, and LLM integration with graph databases such as Neo4j, FalkorDB, or similar technologiesExperience with model serving, monitoring, drift detection, and production ML observabilityExperience with Docker and containerized ML workloadsExperience with MLOps practices and CI/CD for machine learning systemsExperience working with data annotation teams and creating annotationinstructionsExperience with .NET / C#, ASP.NET Core, or integration of ML services into enterprise software platformsExperience building prototypes, demos, and proof-of-concept applications for new AI capabilitiesPublications, patents, or recognized technical contributions in NLP, machine learning, or applied AI are a plus