Applied AI + NLP Engineer

Location: United States - Indianapolis or Chicago Hybrid

Reports to: Chief Technology Officer

Entegrata helps law firms turn data into decision-grade insights. Founded in 2023 and headquartered in Indianapolis, we are a venture-backed SaaS company delivering a turnkey, Azure-based data lakehouse platform purpose-built for law firms. Our platform consolidates siloed legal, operational, and financial data into a single governed source of truth, with AI-powered analytics on top. Our mission is to become the data platform of choice for the top law firms in the world.

Law firms are pouring both time and money into AI, and many efforts stall on the same point: the data underpinning the endeavor. Your job is to fix that.

As our first Applied AI + NLP Engineer, you build the intelligence that turns Entegrata’s governed data platform into an enablement layer for a firm’s entire AI strategy. You will ship the conversational analytics that let firm leaders interact with their data in plain English and get governed, accurate answers they can act on. In addition to helping unlock value related to structured data from firm systems, you will help us turn legal documents into discrete data.

This distillation of documents will help our customers tap into key content with added precision, enabling new insights for customers. You will also build the retrieval and natural-language-to-query capabilities that help firms get more out of the AI tools they already run, and light up new business and practice of law use cases that were not possible before. Because this all sits on top of our governed semantic layer, accuracy, trust, and respect for firm data boundaries are non-negotiable.

This is a senior, hands-on role at the core of our AI enablement roadmap. You will need to be comfortable working where data engineering, AI and product intersect.

• 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 under specified.
• 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…