Lead Applied AI Engineer - Legal EdTech

BARBRI has helped over 1.3 million law students pass the bar exam. Now we're embarking on our AI transformation journey. If you want to combine cutting‑edge AI with a domain that matters, let's talk.

BARBRI is a legal education company with 58 years of legal credibility. For years BARBRI has been changing the lives of pre‑law students, law students and lawyers on how they develop new skills.

Now, with the AI revolution, BARBRI is at a critical juncture. We must simultaneously adapt to new demands of learns from edTech products, break into a rapidly evolving Professional Education market and transform ourselves into an AI‑native organization.

This is a senior IC role where you'll ship real systems: AI‑powered simulations for legal training, workflow automations that move EBITDA, and AI features embedded into existing products. You'll work closely with the Head of AI, with hands‑on coaching as you develop deeper AI/ML expertise. If this works, you'll help shape the team as it grows.

High autonomy. High visibility. Real impact on the product and the business.

• Build and deploy workflow automations (n8n, MS Copilot agents) that solve real problems in Sales, Rev Ops, and Support
• Stand up data pipelines and integrations across our stack (Salesforce, Freshdesk, MS365 Cpilot)
• Get hands‑on with LLM APIs - prompt engineering, RAG basics, evaluation frameworks
• Rapidly prototype and test working solutions (LLM agents, workflow automations, microapps, embedded tools, etc.)

• Own our simulation product — voice AI, LLM orchestration, context management
• Launch AI features into existing BARBRI products — identify high‑value integration points and ship them
• Enable non‑technical teams to build their own AI agents and automations — act as the expert they can lean on
• Architect system integrations across our tooling ecosystem
• As the team grows, provide technical direction — own architecture decisions, mentor new engineers, manage vendors
• Build RAG pipelines, multi‑agent orchestration, and real‑world task integration.
• Support and improve existing Knowledge Retrieval (search, recommendations, course assistant) projects

• AI Simulation Engine: Conversational AI that helps lawyers practice soft skill — (currently in customer discovery)
• Workflow Agents: AI‑powered automations for Sales, Rev Ops, and Support that move EBITDA
• Product AI: Embedding AI capabilities into existing BARBRI learning products

Must Have

• Strong Python skills — data processing, API integrations, backend services
• 5+ years building production backend or data engineering systems
• Hands‑on LLM experience
  : RAG architectures, prompt engineering, evaluation frameworks
• Full‑stack capability
  :
  You can build the API, connect the frontend, and deploy it yourself
• Bias toward shipping
  :
  You optimize for "working in production" over "theoretically optimal"
• Comfort with ambiguity
  : you can take direction early and grow into ownership

Strong Plus

• Azure fluency
  :
  You know your way around Azure infrastructure—App Services, Functions, Cognitive Services, or OpenAI deployments
• Search/retrieval experience
  :
  You've built semantic search, hybrid search, or information retrieval systems
• MS365 ecosystem experience (Copilot Studio, Graph API, Azure OpenAI)
• Workflow automation platforms (n8n, Zapier, or similar)
• Voice/speech AI experience (STT/TTS integration, latency optimization)
• Legal domain knowledge or experience building for regulated industries
• Experience at early‑stage startups or building 0‑to‑1 products
• Node.js/Type Script for backend services

Mindset

• Self‑directed
  :
  You don't wait for tickets. You identify what matters and go build it.
• Pragmatic
  :
  You'd rather ship a good solution today than a perfect one never
• Curious about domain
  :
  Legal ed is niche - you're excited to learn how legal education works
• Low ego
  :
  We're a small team; you'll do unglamorous work alongside interesting problems
• Learning mindset:
  You're excited about staying current in a fast‑moving field
• Communicative
  :
  You can explain technical tradeoffs to non‑technical stakeholders

• Fully remote
  :
  Work from anywhere in the US
• Greenfield ownership
  :
  Define the technical architecture from scratch
• Choo…