LegalOn Technologies is a global leader in legal AI solutions, trusted by more than 8,000 companies and law firms worldwide. The company empowers legal teams to review and negotiate contracts faster, with greater accuracy and confidence. Operating across Japan, the United States, and Europe, LegalOn is the first AI company from Japan to surpass ¥10 billion in annual recurring revenue (ARR) and has raised over $200M from leading investors including SoftBank and Goldman Sachs. It also has one of the largest and most global AI teams of any startups in Japan. In 2025, LegalOn achieved its first M&A by welcoming Germany's Fides Technology into the group, further accelerating global expansion. LegalOn combines LLMs, intelligent Agents and intelligent AI-driven workflows. Beyond legal, the company now offers specialized AI solutions including "CorporateOn," "CXOn," and "DealOn" to support diverse business domains requiring high expertise. In partnership with OpenAI, the company is redefining the legal AI landscape and developing innovative solutions for the future of professional work.
For more details about our company, products, and development organization:
https://legalforce-recruit.notion.site/LegalOn-Technologies-Candidate-Handbook-17d82a58637480d2836ae138e5b5ee95
- Own the design, development, and operation of AI-native features centered on RAG, LLMs, and AI agents, going beyond model implementation to deeply integrate with UI/UX and business workflows to deliver truly usable AI experiences
- Lead end-to-end decision-making across model selection, inference architecture, RAG design, and agent design based on specific use cases, balancing both quality and speed
- Design and implement robust operational frameworks—including evaluation, monitoring, logging, and cost optimization—to continuously improve and maintain a reliable and trustworthy AI experience
- Design and implement highly reliable agent-based LLM workflows for production environments
- Build execution infrastructure for AI agents by integrating with external services and internal APIs
- Evaluate and select appropriate external models, frameworks, and services based on use case fit
- Translate user needs into concrete requirements through stakeholder discussions, and collaborate cross-functionally to design optimal agent-based solutions
- Establish testing frameworks and monitoring systems to evaluate agent performance, and visualize key metrics
- Define development processes and quality standards through code reviews and comprehensive documentation
- Identify and address technical challenges to ensure the accuracy, reliability, performance, and scalability of AI systems, driving continuous improvement
