AI Engineer

Responsibilities

• 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”).
• Refined and optimized 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.

Qualifications

• 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.