Key skills

Applied AI, NLP, and machine learningLarge language models (LLMs)Prompt engineeringRetrieval-augmented generation (RAG)AI agents and tool useInformation extraction document parsingInformation extraction classificationInformation extraction named entity recognitionModel and system evaluationSQLSemantic layersPythonGo, .NET, or Node.jsNatural-language-to-SQLAINLPAgenticData EngineeringAnalytics

About this role

Entegrata is hiring an Applied AI + NLP Engineer to build production AI capabilities on its governed data platform. The role focuses on conversational analytics, natural-language query and retrieval systems, legal document intelligence, and reliable AI workflows.

Responsibilities:

Build systems that translate natural language questions into deterministic analytical workflows and governed queries over structured legal data
Help define and implement semantic models and metadata that enable LLMs and AI agents to reason consistently over operational, business, and legal data
Improve intent recognition, query planning, and ambiguity resolution to deliver accurate, consistent responses—even when user requests are incomplete or underspecified
Develop AI agents capable of accessing, interpreting, and synthesizing structured data and legal content to answer complex business questions
Build orchestration logic that intelligently selects the appropriate data sources, models, tools, and workflows for each request
Implement guardrails that ensure AI responses align with governed business definitions, semantic models, and firm conventions
Build evaluation frameworks, benchmark datasets, and automated tests that continuously improve answer quality, determinism, and reliability
Collaborate with Data Engineering to expose governed metrics, dimensions, relationships, and business logic for AI consumption
Design and build AI pipelines that transform complex legal documents into structured, machine-understandable data
Help design and evolve legal data models that capture the key concepts, relationships, and business meaning contained within legal documents
Develop techniques to identify legal concepts, obligations, timelines, financial terms, parties, and other domain-specific information that populate and enrich those data models
Design confidence scoring, validation, and human review workflows that improve extraction quality and support continuous model refinement
Optimize AI workflows for quality, latency, scalability, and cost across the platform
Partner closely with Product, Data Engineering, and legal domain experts to translate customer needs into production AI capabilities
Stay current with advances in applied AI, LLMs, and agentic systems, bringing practical innovations into production where they create measurable customer value