AI Agent Workflow Intern

Schneider Innovations is a fast-growing global enterprise focused on consumer health, personal protective equipment (PPE), medical devices, and industrial safety solutions. We deliver high-quality, professional-grade consumable products across healthcare, industrial, commercial, and household markets. Our business operates across product development, manufacturing partnerships, global logistics, compliance, and multi-channel distribution. Because our workflows involve many teams, tools, systems, and international markets, we are actively investing in smarter internal processes, automation, and AI-assisted tools to improve speed, accuracy, and operational efficiency. This role is part of that effort. As our systems continue to grow, we are looking for practical builders who can help improve how AI agents, internal tools, and workflow interfaces support real business operations. The work is not about theoretical AI research or model training; it is about making internal systems more reliable, easier to use, and better connected to the way teams actually work. At Schneider Innovations, we value innovation, operational excellence, and continuous improvement. We are building practical technology solutions that help our teams move faster, reduce manual work, and support our long-term growth across global markets. About the role We are looking for an AI Agent System Optimizer Intern to help improve our internal AI agent system. This role will focus on understanding our existing agent workflows, debugging issues, improving system flow, connecting tools, and building lightweight internal interfaces based on team direction. This is not a model training or AI research role, and the intern will not be expected to design the entire AI agent system from scratch. Instead, we are looking for someone who can understand an existing AI workflow system, follow product and technical direction, and make practical improvements that help the system run more smoothly. What you'll do Understand existing AI agent workflows, tool connections, rules, inputs, outputs, and handoff logic Help improve agent workflow based on team-defined improvement directions Debug workflow issues by reviewing output, reproducing errors, and identifying where the process breaks Adjust rules, tool configurations, routing logic, and workflow steps under team guidance Connect agents with internal tools, APIs, databases, dashboards, or communication platforms Build lightweight internal interfaces, dashboards, forms, or testing panels for AI workflows Create simple test cases to check whether agent workflows are working as expected Document workflow structure, known issues, test results, and improvement recommendations Work with product, operations, and engineering team members to turn workflow pain points into practical fixes Qualifications Current student or recent graduate in Computer Science, Information Systems, Data Science, Engineering, Human–Computer Interaction, or a related field Hands‑on experience with AI tools, LLM APIs, agent workflows, automation tools, or tool‑using AI applications Able to understand and modify existing systems rather than needing to build everything from scratch Familiarity with APIs, JSON, webhooks, function calling, or basic tool integrations Comfortable debugging technical issues by reading logs, testing edge cases, and documenting findings Able to build simple internal tools, dashboards, forms, or testing interfaces Strong attention to detail, clear documentation habits, and structured problem‑solving skills Comfortable following product and technical direction while also suggesting practical improvements #J-18808-Ljbffr Schneider Innovations