Principal Agentic AI Design Engineer

CohnReznick• , TX

1d•Remote

About The Position

As CohnReznick grows, so do our career opportunities. As one of the nation’s top professional services firms, CohnReznick creates rewarding careers in advisory, assurance, and tax with team members who value innovation and collaboration in everything they do! CohnReznick helps organizations optimize performance, manage risk, and maximize value through CohnReznick LLP (assurance services) and CohnReznick Advisory LLC (advisory and tax services). Together, the firm provides leaders with deep industry knowledge and relationships, solutions to address clients’ unique business goals and risks, and insight on how emerging market forces can drive opportunity. With offices nationwide, the firm serves organizations around the world as an independent member of Nexia. We currently have an exciting career opportunity for a Principal Agentic AI Design Engineer Senior Manager to join the Strategic AI team. CohnReznick is a hybrid firm and most of our professionals are located within a commutable distance to one of our offices. This position is considered remote which means it does not require job duties be performed within proximity of a CohnReznick office location. However, as a remote employee, you may be required to be present at a CohnReznick office with scheduled notice for client work, team meetings, or trainings. YOUR TEAM. Strategic AI is a newly formed team within CohnReznick's COO organization, built to deliver high-impact, AI-enhanced solutions across the firm. We are an AI-native team by design: from planning and ideation to execution, AI tools are the default—not an afterthought. Our mission is to solve complex business problems through applied AI, embedding intelligent agents, automated workflows, and AI-powered applications directly into how the firm operates and delivers value to clients.

Requirements

10+ years of UX design experience, with at least 3 years focused on AI-powered, conversational, or agentic application design at a senior or director level.
Demonstrated expertise in conversation design: dialogue flow, turn structure, clarification and recovery patterns, voice and tone governance, and designing for the full range of agent states including failure and escalation.
Deep command of agentic AI design patterns: human-in-the-loop design, uncertainty and confidence communication, tool-call transparency, multi-step task progress, and trust calibration in AI interfaces.
Strong design systems ownership: you have built or led a component library that others ship from, and you know how to evolve a design system without breaking what already exists.
Strong visual design fundamentals: typography, layout, color, hierarchy, and information architecture applied to complex enterprise interfaces.
Frontend engineering fluency: you can implement UX changes—copy, interaction states, component variants, visual refinements—directly in code within an established framework and component system.
Comfort working within an existing technical framework and codebase: you contribute to what is already there, learn the conventions, and improve incrementally rather than starting over.
Sufficient understanding of how agentic AI systems work—agent reasoning, tool use, MCP integrations, multi-step execution, and human handoff mechanics—to design experiences that accurately represent those behaviors.
Proficiency in Figma for design system management, high-fidelity prototyping, and interaction specification.
Strong UX research skills: usability testing, task analysis, and the ability to turn qualitative findings into specific design decisions.
Exceptional communication skills—able to present design rationale, run critiques, and advocate for user experience in technical and business conversations.
Bachelor's or Master's degree in Design, HCI, Computer Science, or a related field (or equivalent demonstrable experience).

Nice To Haves

Experience designing AI assistants, copilots, or agent interfaces embedded in enterprise professional workflows—particularly in financial services, professional services, or regulated industries.
Familiarity with Model Context Protocol (MCP) at a conceptual level: understanding what tool calls and external integrations mean for the user-facing experience, even if you are not implementing the MCP server itself.
Advanced conversation design background: voice or multi-modal interaction design, or experience designing AI systems that span multiple channels or modalities.
Experience building or contributing to a pattern library specifically for agentic or conversational AI: documented reusable patterns for streaming output, structured agent responses, and multi-agent handoffs.
Familiarity with motion design and micro-interaction principles applied to enterprise software—knowing when animation communicates state and when it adds noise.
Experience with accessibility auditing and remediation, particularly for dynamic and AI-generated content.
Track record of establishing a design practice within an engineering-led team or startup environment.
Public portfolio demonstrating AI or conversational interface design work, with case studies that show both the design decisions and their rationale.

Responsibilities

Own the end-to-end user experience for CohnReznick's agentic AI applications: defining the vision, principles, and standards that govern how every agent-powered surface looks, behaves, and communicates.
Lead experience design across the full lifecycle of agentic AI products—from discovery and research through concept, prototype, and production—ensuring design quality is maintained at every stage.
Translate complex agentic AI behaviors into experiences that feel intuitive to professional services users who are not AI experts: making agent reasoning, task progress, and decision points legible without oversimplifying them.
Define information architecture and interaction flows for AI-powered workflows, including how users initiate tasks, monitor multi-step agent progress, intervene, correct, and resume.
Partner closely with the Head of AI, AI Solution Engineers, and AI Solution Managers to understand what agent systems can and cannot do, then design experiences that set accurate expectations and build appropriate trust.
Own conversation design for all agentic interfaces: defining dialogue structure, turn-taking patterns, clarification flows, confirmation moments, and error recovery sequences that feel intentional rather than accidental.
Write and govern the voice and tone of AI-generated responses and system messages across agent surfaces—ensuring consistency, clarity, and appropriate register for a professional services context.
Design for the full range of conversation states: successful task completion, partial completion, ambiguity, failure, escalation to human, and graceful exit.
Establish conversation design standards and documentation that other team members can apply consistently when building new agent interactions.
Define and maintain the firm's library of agentic AI design patterns: documented, reusable solutions for recurring UX challenges specific to agent-powered applications.
Establish patterns for surfacing agent reasoning and tool use in ways that build trust without overwhelming users—knowing when to show the work and when to get out of the way.
Design human-in-the-loop patterns: how and when to pause agent execution, surface a decision point, request user confirmation, or escalate to a human—with clear affordances that make the user's role unambiguous.
Develop patterns for communicating uncertainty and confidence: how agents express what they know, what they don't, and what they're assuming, in ways that are honest without being alarming.
Define MCP-informed UX patterns: designing the user-facing experience of tool calls, external data retrieval, and cross-system agent actions in ways that are transparent and controllable.
Build and govern the AI design system for the Strategic AI team: a component library of reusable, production-ready UI elements for agent conversation surfaces, task progress displays, status indicators, error states, and handoff moments.
Implement UX changes directly in code—working within the team's established frontend frameworks and component patterns to ship interaction improvements, copy updates, and visual refinements yourself, without a handoff.
Respect and steward the team's existing technical framework and conventions: contributing design system components that follow established patterns and extend them coherently rather than introducing inconsistency.
Bring visual design craft to implementation: typography, spacing, color, hierarchy, and motion treated as first-class implementation concerns, not finishing touches.
Embed accessibility (WCAG), keyboard navigation, and reduced-motion support into every component from the start.
Define and lead UX research practices for AI interfaces: usability testing, task analysis, and mental model research that generates actionable signal on how users understand and trust agent systems.
Prototype new interaction patterns and conversation flows—using design tools and in-framework code prototypes depending on fidelity needed—and validate them with real users before broader implementation.
Track and respond to user feedback and behavioral signals from deployed agent applications, translating findings into specific, prioritized design improvements.
Stay current with the rapidly evolving field of AI UX: emerging patterns from leading AI products, research on human-AI interaction, and new capabilities that create new design opportunities or challenges.
Set the quality bar for user experience across all Strategic AI products—establishing what 'good' looks like for agentic AI interfaces at CohnReznick and holding that standard consistently.
Mentor engineers and solution managers on interaction design principles and AI UX patterns, building the team's collective design literacy rather than being the sole source of design judgment.
Partner with business stakeholders and AI Solution Managers to conduct discovery—understanding user workflows, pain points, and success criteria—and translate those insights into design direction.
Communicate design decisions and their rationale clearly to engineering, product, and executive stakeholders; advocate for user needs in prioritization and scoping conversations.
Contribute to internal knowledge sharing: document design patterns, research findings, and lessons learned in ways that build the firm's institutional understanding of AI experience design.