What We Do: Florence software advances cures by helping the world’s most important research sites do their best work. Our solutions are now used by over 30,000 research teams in 70 countries around the world—we’re the most widely deployed site workflow tool in the industry. By the end of the decade, we’ll double the pace at which new medicines get to market by doubling the output of trial site teams.

To date, we were named a Deloitte Fast 50 business, G2 Category Leader, an Inc. AJC best place to work, and an Inc. 5000 company five years in a row.

At Florence, we are committed to make the world a better place by accelerating research while providing an environment for our employees where they can be happy in their lives, enjoy their jobs, and grow. What You’ll Bring to The Team: We are looking for a Senior AI Engineer to design, build, and deploy high-quality AI-powered features. This role focuses on owning end-to-end implementation of AI systems within a product area — from prototyping to production — with a strong emphasis on reliability, iteration, and measurable impact.

You will work closely with product and engineering teams to turn ambiguous problems into effective AI solutions, while contributing to best practices and raising the bar for AI development. You Will: End-to-End AI Feature Ownership Design and implement AI-powered features (LLM workflows, copilots, and agent-based systems with tool use and multi-step reasoning) Own the full lifecycle: prototyping → evaluation → production deployment → iteration Ensure solutions are reliable, performant, and aligned with product needs AI System Implementation Build and optimize: Prompt pipelines for specific use cases Retrieval systems (embeddings, chunking, ranking) RAG-based workflows where needed Iterate on outputs to improve quality, accuracy, and consistency Design scalable and cost-efficient AI architectures for production workloads Select and evaluate models (hosted vs open-source) based on use case constraints Agent-Based Systems (AgentCore) Design and build agentic workflows capable of multi-step reasoning and decision-making Integrate agents with tools, APIs, and internal systems to perform real-world actions Implement planning, execution, and reflection loops for complex tasks Manage context, memory, and state across multi-step interactions Balance deterministic workflows vs.

Requirements

Implement monitoring and observability for AI systems in production Implement guardrails for agent actions (tool access control, execution boundaries) Prevent failure cascades in multi-step agent workflows An Ideal Candidate Has: Core AI Skills Strong understanding of LLM capabilities and limitations Experience with prompt engineering and structured output design Hands-on experience with embeddings and vector search Familiarity with RAG architectures and when to apply them Experience designing agent-based architectures (AgentCore concepts) Understanding of tool use, planning strategies, and memory mechanisms in LLM systems Engineering Skills 4+ years of related work experience Solid b