AI Engineer

Scrabble is partnering with an AI Legal Tech startup.

Responsibilities

• Design and deploy production-grade LLM agents capable of operating autonomously for extended periods, including managing state, memory, retries, escalation, and recovery processes.
• Build and maintain an integration layer that abstracts the complexities of various legal case management systems (e.g., Clio, Filevine, Litify, MyCase) into a unified, user-friendly interface.
• Establish and uphold reliability standards through rigorous evaluations, transcript regression testing, and identification of edge cases.
• Refine the conversational abilities of the AI agent, ensuring the voice and tone meet high standards by analyzing transcripts and user interactions.
• Develop a streamlined onboarding process for new law firms, accommodating their unique intake forms, processes, and CMS configurations within days.
• Collaborate directly with customers, founders, and a small team of developers, taking full ownership of the workstream without reliance on managers or handoffs.

Qualifications

• Proven experience shipping LLM-based agents in production environments with real users and handling real failures.
• Strong understanding of agent reliability engineering, including writing evaluations, regression tests, and handling edge cases.
• Experience normalizing and integrating messy third-party systems, such as legacy APIs and diverse configuration requirements.
• Product instincts with a focus on conversational quality; ability to identify and improve conversational missteps.
• High standards for engineering quality and reliability, self-motivated to maintain excellence.
• Excellent communication skills and ability to work collaboratively in a small team.
• Educational qualifications: BTech, BSc, MSc, or equivalent in Computer Science, Engineering, or related fields.

Preferred Skills

• Experience working with legal or case management software integrations.
• Familiarity with conversational AI, voice interfaces, and natural language processing.
• Prior experience in automating workflows within highly fragmented or legacy systems.
• Ability to quickly adapt to new tools and environments, with a focus on rapid onboarding and iteration.

Experience

• Demonstrated 2+ years of relevant experience deploying LLM agents in production with real-world failure handling.
• Experience working with APIs, legacy systems, and multi-system integrations.
• Proven ability to handle complex failure scenarios and improve system robustness.