AI Workflow Developer III (Digital)

CareSource•Dayton, OH

2d•Hybrid

About The Position

The AI Workflow Developer III (Digital) role is responsible for designing, implementing, and maintaining AI-ready workflows, documentation, and agent experiences across our digital products, enabling members, providers, and internal teams to get accurate, contextual answers directly from AI-powered interfaces. This individual will leverage modern AI tools/systems (e.g., Microsoft Copilot Studio or similar agent-building tools) to create and maintain intelligent and capable agents that have context of our applications, workflows, and business rules. The role will be focused on orchestrating existing AI capabilities, structuring content and context for agents, and integrating those agents into user experiences. The ideal candidate has experience working alongside software development teams and understands the software development cycle. They can translate design artifacts and technical system behavior into clear, structured, agent-ready documentation for multiple audiences: members and providers, internal operational teams, and development teams.

Requirements

Bachelor’s degree in computer science, Information Systems, Technical Communication, Human-Computer Interaction, or related field; or equivalent work experience.
4–7 years of experience in software development, product, technical writing, UX, or similar roles working closely with engineering teams.
2+ years of hands-on experience with modern AI tools and platforms (e.g., Microsoft Copilot Studio, Azure OpenAI, OpenAI, or comparable agent/chat platforms).
Practical experience collaborating with frontend development (React, Angular, or Vue) and backend development (Node, Go, or .NET); ability to read and discuss code with engineers.
Experience working with design teams and tooling (e.g., Figma, Sketch, Adobe XD) and translating design artifacts into implemented experiences.
Strong understanding of AI concepts at the application level, including prompt engineering, retrieval, agents, and evaluation of AI outputs.
Demonstrated ability to design clear, structured documentation for multiple audiences (end users, internal operations, developers).
Familiarity with web technologies, frontend frameworks (React, Angular, or Vue), and backend platforms (Node, Go, or .NET), with the ability to collaborate effectively with developers.
Comfort working within an agile software development lifecycle, including requirements discovery, backlog management, and iterative delivery.
Excellent written and verbal communication skills, with the ability to translate complex technical concepts into accessible language.
Strong collaboration skills, with experience co-creating solutions with design, product, engineering, and operations teams.
Curiosity and adaptability, with a bias toward experimentation and continuous learning in a fast-evolving AI landscape.

Nice To Haves

Experience in a regulated industry (e.g., healthcare, financial services, insurance) is preferred but not required.
Certifications related to Microsoft Azure, Microsoft Copilot, or other major cloud/AI platforms are a plus.
Relevant UX, product, or technical communication certifications are beneficial but not required.

Responsibilities

AI Agents & Architectural Design - Design, build, and manage AI agents using platforms such as Microsoft Copilot Studio or comparable tools.
- Define agent goals, conversation flows, and guardrails that align with solution demands.
- Structure prompts, context windows, and knowledge sources so agents can reliably answer questions about our applications and processes.
- Collaborate with product, design, and engineering teams to embed agents and agent workflows into web, portal, and internal tools in a way that feels natural and trustworthy to users.
AI-Ready Documentation & Knowledge Management - Create and maintain AI-ready documentation covering our applications, including user-facing help, internal operations guides, and technical reference materials for development teams.
- Design documentation structures (e.g., information architecture, metadata, chunking strategies) that optimize retrieval and relevance for AI agents.
- Use generative AI tools to draft, refactor, and update documentation while maintaining accuracy, clarity, and consistency with brand and compliance requirements.
- Establish processes to keep documentation synchronized with product releases, configuration changes, and new features.
- Partner with application owners and subject matter experts to validate content and ensure it reflects current behavior.
Software Development Workflow & Integration - Work closely with software engineering teams to understand system behavior, data flows, and edge cases, and translate those into documentation and agent knowledge.
- Participate in agile ceremonies (e.g., backlog refinement, sprint planning, demos) to stay aligned with upcoming features and changes.
- Contribute lightweight code changes where appropriate (e.g., UI content updates, documentation hooks, telemetry events, simple integration logic) in collaboration with engineering teams.
- Help shape AI-led workflows within the development lifecycle (e.g., AI-assisted design reviews, test generation, release notes, and change summaries).
Frontend & Backend Collaboration - Collaborate with frontend engineers working in frameworks such as React, Angular, or Vue to integrate AI agents and contextual help into user interfaces.
- Work with backend teams building services in Node, Go, and especially .NET, to understand APIs, domain models, and business rules that agents and documentation must reflect.
- Interpret design artifacts (e.g., Figma, design systems, interaction specs) and partner with designers to ensure AI experiences align with visual and interaction standards.
- Provide feedback to design and engineering teams on how layouts, flows, and content can better support AI-driven self-service.
Continuous Improvement & Governance - Monitor usage, performance, and quality of AI agents and documentation, using analytics, feedback, and qualitative review to identify improvements.
- Help define standards, patterns, and guidelines for AI-ready documentation, prompt design, and agent configuration across the organization.
- Stay current with emerging AI tools, platforms, and best practices, and recommend pragmatic improvements to our AI experience stack.
- Support training and enablement for internal teams on how to work with AI agents and contribute to AI-ready content.