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Legora is an AI-native workspace for legal professionals, trusted by 1,000+ customers including major law firms and enterprises across 50+ countries. The company has scaled to $100M+ ARR and operates teams across Europe, North America, and APAC.
The AI Enablement team is responsible for making Legora AI-native by default. You will take broad ownership of how AI works inside the company and the platforms built to enable it. This includes building internal agents and tools (such as Leya, an internal knowledge agent in Slack), equipping other teams to build their own AI products, and owning the standards, access models, and guardrails that allow safe, scaled AI adoption.
Key responsibilities:
- Build internal agents, tools, and platforms as products, not projects. Own quality, reliability, and business impact end-to-end
- Take broad goals, determine actual requirements, and break work into executable tasks independently
- Own what you build from architecture through production reliability and adoption
- Shape the product roadmap and advocate for priorities
- Design foundational systems: knowledge architecture, access and permissions models, evaluation and monitoring, risk-scaled guardrails
- Work directly with end users across the entire company to understand needs and validate solutions
The challenge: Legora is a company of ~1,000 people already fluent with AI tools. There is no adoption problem and nowhere to hide behind change management. If something isn't used, it wasn't good enough. Your colleagues know what good looks like and will build it themselves in an afternoon if your solution doesn't meet that bar. The mandate is large and the team is small—the real work is getting infrastructure, access models, and architecture right at company scale without a specification.
Requirements:
- Strong engineering fundamentals; you build production systems, not prototypes
- Real depth with LLM-based systems: agents, retrieval, evaluation, tool use. You understand where these systems break and design for it
- Ownership instinct: you don't wait for specifications. When requirements are unclear or wrong, you say so and propose better alternatives
- Three kinds of simultaneous understanding: technical, domain, and business. You can discuss architecture and business needs and connect them
- Openness to feedback, building on others' ideas, changing your mind with evidence, and pushing for the strongest outcome
- Comfort with ambiguity: new team, fast growth, revisited decisions as you learn
- TypeScript proficiency (primary language; shared patterns live here)
- No aversion to infrastructure: you own it, so Terraform and cloud providers are part of your work
- Daily work with code agents, not as experiment but as standard practice
