Design, develop, and deploy AI-powered solutions including custom GPTs, copilots, and agentic workflows using enterprise LLM platforms and APIs.
Build Retrieval-Augmented Generation (RAG) pipelines, vector search capabilities, and secure data connectors to enable Verathon-owned data usage.
Collaborate with AI Business Partners and other departmental representatives to translate functional requirements and use-case concepts into technical implementations.
Translate business-defined context into structured prompts, system instructions, agent behaviors, and RAG retrieval logic (“technical context engineering”).
Refine and optimize context injection strategies (prompt templates, chains, tools, memory, retrieval parameters) to improve solution reliability and accuracy.
Prototype rapidly, iterate based on user feedback, and deliver production-ready AI solutions with appropriate guardrails.
Develop reusable prompts, templates, automations, and components to accelerate future AI solution development.
Integrate AI tools with Verathon systems (e.g., Salesforce, Epicor, MasterControl, HRIS) following architecture, security, and compliance standards.
Partner with the AI Solutions Architect to evaluate and implement new AI tools and ensure alignment with Verathon’s AI Policy and Roper guidelines.
Document solutions clearly and support functional teams through adoption, training, and scaling of deployed AI tools.

Bachelor’s degree in computer science, engineering, information systems, data science, or related field.
3–7 years of experience in software development, automation engineering, data engineering, or applied AI development.
Hands-on experience with Python, REST APIs, and modern AI/LLM frameworks (e.g., LangChain, Semantic Kernel, LlamaIndex).
Experience configuring and deploying AI tools such as ChatGPT Enterprise, Copilot, Claude, or similar enterprise AI platforms.
Understanding of Retrieval-Augmented Generation (RAG), vector databases, and prompt design best practices.
Familiarity with integration patterns for enterprise systems and cloud environments (Azure/AWS).
Demonstrated ability to rapidly experiment, prototype, and iterate AI solutions based on business feedback.
Strong collaboration skills and comfort working closely with business and technical stakeholders.

AI Engineer

Key skills