Principal Engineer, NLP

** Our company*
* At Teradata, we believe that people thrive when empowered with better information. Teradata Autonomous Knowledge Platform activates enterprise intelligence by unifying data, knowledge and business context to achieve tangible outcomes. With Teradata, organizations can provide agents with full context for impact when it matters. Our solution lets businesses connect and scale on premises, in the cloud, or through a hybrid approach.

Teradata delivers real business value with AI.

** What You'll Do*
* We are building the next generation of AI-powered knowledge, reasoning, and autonomous agent platforms that operate at enterprise scale. We are seeking an exceptional  
** Principal NLP Engineer
** to own and lead the design, development, and deployment of advanced natural language technologies powering semantic and context graphs, context analysis, intelligent search, retrieval, question answering, knowledge extraction, reasoning systems, AI agents, and enterprise copilots for Teradata Context Engine, our Autonomous Knowledge Platform. Context Engine is a platform that converts enterprise data estates into governed, agent-ready knowledge. Built for organizations where AI reliability and auditability are non-negotiable, Context Engine enables companies to deploy AI workflows on the data they already own - without rebuilding their infrastructure, without retraining their models, and without waiting for generic AI synthesis to learn the business terminology that took their analysts years to master.

This is a deeply hands-on technical leadership role for a recognized expert in Natural Language Processing who combines strong research foundations with proven experience shipping production systems that serve millions of users and process billions of documents. You will work at the intersection of large language models, information retrieval, knowledge graphs, semantic understanding, and agentic AI.

The ideal candidate has a track record of driving breakthrough NLP capabilities from research to production and possesses deep expertise across classical NLP, modern transformer architectures, retrieval-augmented generation (RAG), multimodal understanding, information extraction, and large-scale language model systems.

Technical Leadership

+ Define and execute the strategic roadmap for NLP and language understanding capabilities across our Knowledge Platform.

+ Serve as the technical authority for large-scale NLP architecture, algorithms, and production deployment.

+ Drive innovation in enterprise language understanding, semantic search, retrieval systems, knowledge extraction, and AI reasoning.

NLP and Language Intelligence

+ Design and build advanced NLP systems for:

+ Entity extraction

+ Relationship extraction

+ Information extraction

+ Document understanding

+ Semantic indexing

+ Context understanding and representation

+ Text classification

+ Topic modeling

+ Summarization

+ Question answering

+ Conversational AI

+ Knowledge grounding

+ Multi-document reasoning

+ Extracting context from multi-modal sources of language

+ Multi-lingual solutions

+ Machine translation

+ Develop systems that transform unstructured enterprise content into structured knowledge assets.

+ Advance techniques for long-context understanding, document intelligence, and multi-hop reasoning.

+ Build domain-adaptive language understanding models across diverse enterprise data sources.

+ Develop solutions with highest quality and performance

+ Develop solutions that are tested and validated

Large Language Models

+ Design and optimize applications built on frontier foundation models.

+ Build advanced retrieval-augmented generation architectures for enterprise-scale environments.

+ Develop agentic workflows that leverage LLMs for planning, reasoning, execution, and tool usage.

+ Create evaluation methodologies that measure factuality, grounding, trustworthiness, and task completion.

+ Improve model performance through prompt engineering, fine-tuning, continued pretraining, synthetic data generation, and reinforcement-learning-based approaches.

Knowledge and Semantic Systems

+ Design NLP pipelines that power enterprise knowledge graphs and semantic layers.

+ Develop ontology-aware language understanding systems.

+ Build entity resolution, canonicalization, taxonomy mapping, and concept extraction capabilities.

+ Connect language understanding with graph-based reasoning and enterprise knowledge discovery platforms.

Platform and Scale

+ Build highly scalable distributed NLP systems processing…