About the job
Ironclad is the premier AI contracting platform that revolutionizes the way agreements are treated as valuable assets. Contracts are processed more swiftly, insights emerge instantaneously, and workflows are seamlessly advanced—all while maintaining your control. Whether you are engaged in buying or selling, Ironclad streamlines the complete process on a single intelligent platform, granting leaders the visibility necessary to stay ahead of the curve. This is why the most innovative organizations in the world, such as OpenAI, the World Health Organization, and the Associated Press, trust Ironclad to expedite their operations.
Consistently recognized as an industry leader, Ironclad has been named a Leader in the Forrester Wave and the Gartner Magic Quadrant for Contract Lifecycle Management. We have also been acknowledged as a Great Place to Work by Fortune and one of Fast Company’s Most Innovative Workplaces. With backing from top investors including Accel, Y Combinator, Sequoia, BOND, and Franklin Templeton, we are poised for continued growth. For more details, visit www.ironcladapp.com or follow us on LinkedIn.
AI Engineering at Ironclad
At Ironclad, we are accelerating our AI initiatives to transform how contracts are created, managed, and executed throughout organizations. We are in search of a Generative AI Application Developer to swiftly develop and implement high-impact legal AI product features.
In this role, you will be the go-to expert for AI feature development, leveraging our existing AI platform. You will establish a Prompt Strategy, utilizing established Retrieval-Augmented Generation (RAG) services and Large Language Model (LLM) APIs to create intelligent product capabilities such as summarization and redlining.
Collaboration with product managers and designers will be key as you translate user requirements into production-ready LLM application logic. Your success will be determined by your ability to design optimal context inputs and craft effective prompts that maximize the accuracy, relevance, latency, and reliability of user-facing features.

